https://llmobile.news/categories/models/A concise news ticker covering AI on mobile devices, local models, apps and hardware.en-GBhttps://llmobile.news/ticker/edge0-35b-iphone-demo/https://llmobile.news/ticker/edge0-35b-iphone-demo/Thu, 10 Sep 2026 16:08:02 +0200Samuel Zeng has released Edge0, an open-source framework for streaming mixture-of-experts inference, under Apache-2.0. In his launch post he reports a 35-billion-parameter model running on an iPhone at 1 to 2.5 GB of peak memory without a cloud connection. The repository describes three techniques. Expert weights are streamed from storage on demand, so peak memory is bounded by the active set. A trained prerouter predicts expert routing one step ahead so that loads overlap the forward pass, which the project credits with up to 59 percent higher throughput. Recover-LoRA pairs an int4-quantised frozen base with distilled LoRA adapters. Two tiers are published. edge0-35b has 40 layers and 256 experts, a checkpoint of roughly 23 GB and about 2.9 GiB of peak active memory. edge0-8b has 24 layers and 128 experts, a 4.2 GB checkpoint and about 1.0 GB peak. On a Mac mini M4 Pro with 24 GB, the project measures 14.9 to 17.7 tokens per second of decode for the 35B tier and 23.9 to 25.3 for the 8B tier, with prefill at 113 tokens per second cold and 140 warm for 35B. Quality loss against FP16 baselines is given as 3.9 points on average for the 35B tier and 2.8 points for the 8B tier. The repository states that the MLX backend runs on macOS with Apple Silicon, on M1 through M4, and requires Python 3.10 or newer. Your browser does not support this video. This video could not be loaded. Use the link below to open it directly. Demo by Samuel Zeng. Watch original on X Source: https://x.com/SamuelZengML/status/2097861839287927139 Read the article: https://llmobile.news/ticker/edge0-35b-iphone-demo/iPhoneApple SiliconOpen weightsMixture of expertsMemoryhttps://llmobile.news/ticker/minicpm5-2b/https://llmobile.news/ticker/minicpm5-2b/Mon, 07 Sep 2026 10:00:00 +0200OpenBMB has released MiniCPM5-2B, published on Hugging Face and dated 7 September 2026 in the project changelog. The model card describes a dense Transformer “built for on-device, local deployment, and resource-constrained scenarios”, with 2,516,756,480 parameters of which 1,981,982,720 are non-embedding. The architecture uses 42 layers with grouped-query attention at 16 query heads and 2 key-value heads, and supports a context length of 131,072 tokens. It is the second model in the MiniCPM5 series after MiniCPM5-1B, scaling the same recipe to a larger footprint. OpenBMB reports an average score of 53.9 across its evaluation dimensions, including 69.1 on LiveCodeBench, 86.5 on AIME 2025 and 68.1 on NoLiMa. The release is licensed under Apache-2.0 and ships in BF16, GGUF, GPTQ 4-bit, MLX for Apple Silicon and LiteRT-LM for mobile, plus a DSpark draft model for speculative decoding. Source: https://huggingface.co/openbmb/MiniCPM5-2B Read the article: https://llmobile.news/ticker/minicpm5-2b/OpenBMBOpen weightsQuantisationLiteRThttps://llmobile.news/ticker/gemini-nano-4-first-devices/https://llmobile.news/ticker/gemini-nano-4-first-devices/Thu, 27 Aug 2026 16:00:00 +0200Google’s ML Kit GenAI documentation now lists the first devices running nano-v4, 9to5Google reports. The list covers the Pixel 11, Pixel 11 Pro, Pixel 11 Pro XL and Pixel 11 Pro Fold, plus Samsung’s Galaxy Z Flip8, Galaxy Z Fold8 and Galaxy Z Fold8 Ultra. The same documentation sets Nano v3 or greater as the requirement for Gemini Intelligence, Google’s on-device feature set. According to the report, that requirement first appeared in May 2026, was removed, and has now been reinstated. Listed hardware requirements include 12 GB or more of RAM, a qualified flagship system-on-chip, five or more OS upgrades and six years of security support. Gemini Intelligence features named in the report include Rambler and Proactive Assistance on Pixel 11, and task automation across more than 40 apps on Samsung’s foldables. Source: https://9to5google.com/2026/08/27/gemini-intelligence-nano-4/ Read the article: https://llmobile.news/ticker/gemini-nano-4-first-devices/GoogleAndroidPixelSamsungGemini Nanohttps://llmobile.news/ticker/artificial-analysis-mobile-benchmark/https://llmobile.news/ticker/artificial-analysis-mobile-benchmark/Mon, 24 Aug 2026 15:00:00 +0200 Chart: Artificial Analysis. Artificial Analysis has published a benchmark of small language models running locally on an iPhone 17 Pro. The study covers 41 quantised builds, 33 of which ran successfully on the device. Models were executed with llama.cpp at 4-bit quantisation or smaller, and had to fit within 8 GB of memory including the KV cache at 8K context. Artificial Analysis states that the inference benchmarking is operated in partnership with Liquid AI. Nanbeige4.2-3B and LFM2.5-2.6B tied for the highest intelligence score at 63. On speed and memory the two differ sharply: LFM2.5-2.6B completed the test prompt in 8.0 seconds using 2.3 GB, against 21.4 seconds and 4.0 GB for Nanbeige4.2-3B. Ornith-1.0-9B scored 62 and Qwen3.5 9B (Reasoning) 61. End-to-end generation time for a 1,024-token prompt plus 256 output tokens ranged from 0.9 seconds for LFM2.5-230M to 26.7 seconds for Falcon-H1R-7B. Peak in-test memory at 4K context ran from 0.4 GB to 6.9 GB. The intelligence index combines tool calling, instruction following, knowledge, scientific reasoning and mathematics. Artificial Analysis notes that context limits, not parameter count alone, held back several 9B models, which frequently hit a 16K ceiling. Chart: Artificial Analysis. Source: https://artificialanalysis.ai/articles/mobile-phone-intelligence-inference Read the article: https://llmobile.news/ticker/artificial-analysis-mobile-benchmark/BenchmarksiPhoneQuantisationllama.cppLiquid AIhttps://llmobile.news/ticker/lfm2-5-2-6b/https://llmobile.news/ticker/lfm2-5-2-6b/Tue, 04 Aug 2026 17:00:00 +0200Liquid AI has released LFM2.5-2.6B, a 2.6-billion-parameter model built on its LFM2 architecture for edge deployment. The company reports 30 tokens per second on a phone, with CPU inference staying under 2.5 GB of memory. Its published chart identifies the device as a Qualcomm Snapdragon Galaxy (SM-S948U1) and puts memory use at 2,448 MB at Q4_K_M quantisation with a 4K-token input context. Chart: Liquid AI. On other CPUs Liquid AI cites 220 tokens per second on an M5 Max and 113 tokens per second on a Ryzen AI Max+ 395. The model was pre-trained on roughly 34 trillion tokens, uses a 128K vocabulary, and its mid-training included a dedicated 128K context-extension phase. Liquid AI positions the model for agentic use, describing planning, tool calling and multi-step tasks, and states that it is competitive with, and often outperforms, models nearly four times its size on instruction-following and tool use, while larger models keep an advantage in coding. Weights are on Hugging Face in BF16, GGUF for llama.cpp, and ONNX. Source: https://www.liquid.ai/blog/lfm2-5-2-6b Read the article: https://llmobile.news/ticker/lfm2-5-2-6b/Liquid AIOpen weightsLFMBenchmarksAgentshttps://llmobile.news/ticker/apertus-mini/https://llmobile.news/ticker/apertus-mini/Wed, 27 May 2026 23:40:00 +0200Researchers working in the Swiss AI Initiative published a technical report on May 27, 2026 describing three language models distilled from Apertus 8B, at 0.5B, 1.5B and 4B parameters. The authors are based at EPFL, ETH Zurich and ISTA, and the models sit on Hugging Face as the Apertus Mini collection. They inherit what the project calls a fully open and compliant release, meaning published weights, published data pipeline and published recipe, with pre-training restricted to openly available data that honours robots.txt opt-outs. The model card lists 1,811 natively supported languages. Each instruction-tuned model also ships in MLX format at 3, 4 and 6 bits, which the authors aimed at Apple devices and at memory-limited mobile and edge deployment. The 4-bit 4B weights are a 2.59 GB file against 7.65 GB for the same model in bfloat16, and the 1.5B and 0.5B come to 1.08 GB and 0.31 GB. The authors build the quantised copies with quantisation-aware distillation, a short recovery run that retrains the compressed weights against the 8B model’s own output distribution, and report 90% to 104% of the unquantised few-shot accuracy on ARC, HellaSwag, MMLU and WinoGrande. Separate FP8 and NVFP4 checkpoints target NVIDIA GPUs instead. The base model table averages ARC, HellaSwag, WinoGrande, XNLI, XCOPA and PIQA, and puts the three sizes at 51.79, 56.66 and 61.53 against 64.96 for the 8B teacher. The same table gives the Qwen3 base models 52.23, 57.51 and 62.14 at comparable sizes, so the distilled models land just under them on the average while leading on the two cross-lingual sets, 45.03 against 43.00 on XNLI and 63.82 against 61.82 on XCOPA at 4B. On a multilingual average of MMLU, TruthfulQA, ARC, instruction following and LogiQA for the instruction-tuned models, the authors report 0.318, 0.382 and 0.473 against 0.401, 0.457 and 0.521 for Qwen3 and 0.497 for Gemma 3 4B, and write that the family is weaker at instruction following and maths. Each model saw 1.7T tokens taken from the final and most heavily filtered phase of the original Apertus data, with no new sources added, packed into 4,096-token sequences and trained on 64 GH200 GPUs at the Swiss National Supercomputing Centre. The cost table puts the 4B run at 2.0E22 FLOPs and the single pass that produced the teacher’s logits for all three students at 1.4E22, against 3.7E23 FLOPs for the original Apertus 8B pre-training on 15T tokens, which the authors describe as making the whole family cost under 12% of the teacher. Swiss AI released 24 checkpoints in total, base, instruction-tuned and quantised, under the Apache 2.0 licence with an acceptable use policy attached, along with the training, post-training and quantisation code. Source: https://arxiv.org/abs/2605.29128 Read the article: https://llmobile.news/ticker/apertus-mini/Swiss AIDistillationQuantisationOpen weightsApple Siliconhttps://llmobile.news/ticker/mobilemoe/https://llmobile.news/ticker/mobilemoe/Tue, 26 May 2026 19:58:00 +0200Meta AI published MobileMoE on May 26, 2026, a family of three language models for phones that hold 1.3B, 2.8B and 5.3B parameters but run only 272M, 528M and 922M of them on any given token. Each one is a sparse mixture of experts, meaning the feedforward block in every layer is split into 60 small networks plus one always-on shared network, and a router picks 4 of the 60 for each token while the rest sit idle. Per-token compute therefore follows the active count, and the memory footprint follows the total. That split is what makes the models awkward to host on a phone. The authors quantise all three to 4 bits with quantisation-aware training, which puts the weights at 0.68 GB, 1.48 GB and 2.75 GB, because every expert has to be reachable even though only a few run per token. On a Samsung Galaxy S25 they measure peak resident memory during inference at 1.49 GB for the smallest model at an 8k context against 1.91 GB for the dense MobileLLM-Pro, and at 4.71 GB for the largest. Sizing the whole expert set to fit phone DRAM is the opposite of the route Edge0 takes, which streams expert weights off storage to keep a 35B model inside a couple of gigabytes. The paper reports that peak memory under real prompts runs 1.2 to 2.1 times higher than under repeated dummy tokens for the mixture-of-experts models, while staying flat for the dense baseline, because varied input routes to more experts and pulls more of them into RAM. Meta AI therefore profiled with real code, knowledge and maths prompts and calls dummy-prompt figures a lower bound rather than a measurement. The authors ran the models through ExecuTorch on the Galaxy S25 with a Snapdragon 8 Elite on 4 CPU threads and on an iPhone 16 Pro with an A18 Pro on 2 CPU threads, after writing a fused mixture-of-experts operator because mobile CPU backends ship none. On a 512-token prompt they measure the smallest model decoding at 130.0 tok/s on the Galaxy S25 against 56.5 for MobileLLM-Pro, and at 180.5 tok/s on the iPhone 16 Pro against 55.1. Time to first token on a 1k prompt drops from 4.26 s to 2.01 s on the Galaxy and from 6.03 s to 2.14 s on the iPhone. Across both phones, CPU and GPU, and the XNNPACK and MLX backends, Meta AI reports the smallest model running 1.8 to 3.8 times faster at prefill and 2.2 to 3.4 times faster at decode than the dense baseline at comparable 4-bit weight memory. Across 14 benchmarks the authors score the instruction-tuned models at 46.7, 55.3 and 60.1 on average at 16-bit precision, against 42.7 for the dense Qwen3.5 0.8B, 44.0 for Gemma 3 1B, 46.9 for Llama 3.2 1B and 50.8 for Qwen3.5 2B, the last of which carries 1.9B active parameters. The largest model reaches 60.1 against 55.6 for Ai2’s OLMoE-1B-7B, with 30 percent fewer active and 23 percent fewer total parameters. After 4-bit quantisation those averages fall to 44.0, 52.5 and 57.8, which leaves the smallest model 1.5 points behind MobileLLM-Pro at 45.5 while scoring 9.4 points higher on MMLU. Meta AI states that the training data across all four stages comes from openly licensed public datasets, names no weight release, and lists mobile NPU deployment, expert pruning and mixed-precision quantisation as further work. Chart: Meta AI. The authors' own comparison of instruction-tuned models at 16-bit precision, with the horizontal axis giving total parameters alongside the 4-bit weight footprint those imply. Source: https://arxiv.org/abs/2605.27358 Read the article: https://llmobile.news/ticker/mobilemoe/MetaMixture of expertsMemoryQuantisationiPhonehttps://llmobile.news/ticker/litert-lm-on-device-genai/https://llmobile.news/ticker/litert-lm-on-device-genai/Tue, 19 May 2026 18:00:00 +0200Google has detailed LiteRT-LM, the runtime it uses to run Gemma 4 on-device in Chrome, ChromeOS, the Pixel Watch and the Google AI Edge Gallery app. It sits on top of LiteRT, formerly TensorFlow Lite, and adds GenAI libraries over the XNNPACK and ML Drift kernels. Running Gemma 4 E2B without multi-token prediction, Google measures 52 decode tokens per second on the Android GPU through OpenCL, 56 on iOS through Metal, and up to 76 in the browser on WebGPU. The Android figures come from a Samsung S26 Ultra, iOS from an iPhone 17 Pro, and web from Chrome on a 2024 MacBook Pro with an M4 Max. Chart: Google. The published comparison puts LiteRT-LM prefill at 3,808 tokens per second against 292 for llama.cpp on the Android GPU, 2,878 against 1,493 for MLX on iOS, and 4,182 against 1,127 for ONNX on the web. Decode figures in the same chart are 52 against 19, 56 against 32 and 76 against 29. Multi-token prediction raises decode further. Google reports up to a 2.2x speedup, taking Gemma 4 E2B from 52 to 85 tokens per second and E4B from 21 to 47, measured on a Samsung S26 Ultra GPU. The drafter and the primary model run on the same hardware block so the shared KV cache and activations stay in local memory. Chart: Google. On memory, LiteRT-LM keeps per-layer embeddings out of memory and loads image and audio encoders only when a task needs them. Google states that the 2.58 GB Gemma 4 E2B model runs with a physical footprint of 607 MB on Apple mobile CPUs using XNNPACK weight caching. For agentic use the runtime supports Thinking Mode, which developers can stream to the interface or strip to save KV cache space: By dedicating a scratchpad for step-by-step reasoning before the model commits to an action, LiteRT-LM can significantly improve the output quality. Constrained decoding enforces JSON schemas on tool payloads, and native function calling pauses execution, returns the tool-call request to the app and resumes on the tool’s output. Session save and restore serialise KV cache state so long conversations skip the prefill phase on return. Beyond the existing Kotlin and C++ interfaces on Android, Google is adding an open-source Swift API for Apple platforms and a JavaScript API for the web through WebAssembly and WebGPU. Source: https://developers.googleblog.com/blazing-fast-on-device-genai-with-litert-lm/ Read the article: https://llmobile.news/ticker/litert-lm-on-device-genai/GoogleLiteRTGemmaBenchmarksAndroidhttps://llmobile.news/ticker/hunyuan-hy-mt-1-5-offline-translation/https://llmobile.news/ticker/hunyuan-hy-mt-1-5-offline-translation/Wed, 29 Apr 2026 15:55:00 +0200Tencent Hunyuan has open-sourced Hy-MT1.5-1.8B-1.25bit, a translation model that runs fully offline on a phone. Quantisation to 1.25 bits cuts the file from 3.3 GB at FP16 to 440 MB; a 2-bit build is 574 MB. The announcement states the result is 25 percent smaller and around 10 percent faster than prior 1.67-bit approaches with no loss of accuracy. Chart: Tencent Hunyuan. The compression uses Sherry, a ternary quantisation framework with 3:4 fine-grained sparsity: of every four weights, the three most important are stored in 1 bit as -1 or +1 and the fourth is zeroed. A custom STQ kernel aligns the format to the SIMD instruction set of mobile CPUs. Running it requires llama.cpp with STQ1_0 kernel support, added in pull request 22836, and the model card links a side-by-side speed demo recorded on a Snapdragon 888 with 8 GB of RAM. The model covers 33 languages, 5 dialects and minority languages, and 1,056 translation directions, including Tibetan and Mongolian. On the FLORES-200 benchmark Tencent reports it ahead of Tower-Plus-72B, Qwen3-32B, Microsoft Translator and Doubao on Chinese-to-foreign pairs, and the announcement claims it outperforms Google Translate and matches 235B-scale models. Chart: Tencent Hunyuan. Weights are on Hugging Face alongside an Android demo APK for offline translation. The code is released under the AngelSlim licence rather than a standard open-source licence. Source: https://x.com/TencentHunyuan/status/2049487799850840334 Read the article: https://llmobile.news/ticker/hunyuan-hy-mt-1-5-offline-translation/TencentOpen weightsQuantisationllama.cppTranslationhttps://llmobile.news/ticker/small-vlms-video-compressors/https://llmobile.news/ticker/small-vlms-video-compressors/Thu, 09 Apr 2026 13:40:25 +0200Researchers at Meta AI and KAUST published Tempo on April 9, 2026, a way to hand a long video to a small vision-language model first and let a larger language model answer the question afterwards. On LVBench, a benchmark whose clips average 4101 seconds, the authors report their 6B system scoring 52.7 against 48.2 for VideoChat-Flash 7B, the strongest specialised baseline in their table, and against 30.8 for GPT-4o and 33.1 for Gemini 1.5 Pro. The small model does the reading and the large one does the reasoning. A Qwen3-VL-2B-Instruct compressor takes an eight-frame chunk of video together with the user’s question and writes out a short run of memory tokens that hold what the chunk contains, and a Qwen3 4B language model then reads the whole condensed sequence with timestamps attached and produces the answer. A linear projector joins the two, for 6B parameters in total. The compressor is the part that could in principle sit on a phone, since it never sees more than a few seconds of video at a time. How many tokens each chunk gets is decided during the same forward pass. The authors insert a yes or no question about whether the segment is relevant to the query, read the difference between the two logits as a score, and keep more memory tokens for chunks that score high, spending between 0.5 and 16 tokens per frame under a fixed overall budget of 4K or 8K visual tokens. On LVBench under the 4K budget the measured average comes to 2.9 tokens per frame. The tighter budget also scores better than the looser one on that benchmark, 52.7 against 52.3, and on the long subset of Video-MME, 57.8 against 57.0. Raising the cap to 2048 frames and a 12K budget brings LVBench to 53.7. Diagram: Fei et al., Figure 2 of the paper, licensed CC BY 4.0. On the other three benchmarks the authors report 67.8 on Video-MME under the 4K budget against 65.3 for VideoChat-Flash and 61.9 for the Qwen3-VL 2B model the compressor starts from, plus 75.6 on MLVU and 65.1 on LongVideoBench. All of these are accuracy scores run through lmms-eval, not speed or memory figures. Nothing in the paper was measured on a phone, a tablet or any other edge device. Training used 32 and then 64 Nvidia H100 GPUs, the paper reports no latency, throughput or memory footprint for the compressor on mobile hardware, and the on-device case rests on the size of the model rather than on a measurement. The authors state that all training datasets they used are publicly available, and they link code and a project page from the paper, with the repository carrying an Apache 2.0 license. They describe no weights release in the paper itself. Their stated next steps are to let the compressor decide for itself when it has gathered enough evidence to stop, which they say would bottleneck inference latency as things stand. Source: https://arxiv.org/abs/2604.08120 Read the article: https://llmobile.news/ticker/small-vlms-video-compressors/MetaQwenResearchBenchmarksDistillationhttps://llmobile.news/ticker/gemma-4-edge-agent-skills/https://llmobile.news/ticker/gemma-4-edge-agent-skills/Thu, 02 Apr 2026 18:00:00 +0200Google DeepMind has launched Gemma 4 under the Apache 2.0 licence, and the Google AI Edge team has described how the E2B and E4B variants run on edge hardware. The models support more than 140 languages and a 128K context window. Gemma 4 enables multi-step planning, autonomous action, offline code generation, and even audio-visual processing, all without specialized fine-tuning. Image: Google. The Google AI Edge Gallery app on iOS and Android gains Agent Skills, which Google calls one of the first applications to run multi-step, autonomous agentic workflows entirely on-device. The examples given are querying Wikipedia, turning speech input into charts and flashcards, pairing photos with generated music, and building a small app that plays animal calls through conversation alone. For in-app deployment Google points to LiteRT-LM. It reports that Gemma 4 E2B runs in under 1.5 GB of memory on some devices using 2-bit and 4-bit weights with memory-mapped per-layer embeddings, and that GPU optimisations process 4,000 input tokens across two skills in under 3 seconds. Constrained decoding enforces structured output, and dynamic context lengths let a single model span CPUs and GPUs. On smaller hardware, LiteRT-LM reaches 133 prefill and 7.6 decode tokens per second on a Raspberry Pi 5 CPU, and 3,700 prefill and 31 decode tokens per second on the NPU of a Qualcomm Dragonwing IQ8, the processor behind the Arduino VENTUNO Q. Gemma 4 is available with CPU and GPU support on Android and iOS, system-wide on Android through the new AICore Developer Preview, on Windows, Linux and macOS via Metal, and in the browser through WebGPU. Google also released a Python package and a litert-lm CLI for Linux, macOS and Raspberry Pi, with tool calling included. Source: https://developers.googleblog.com/bring-state-of-the-art-agentic-skills-to-the-edge-with-gemma-4/ Read the article: https://llmobile.news/ticker/gemma-4-edge-agent-skills/GoogleGemmaAgentsOpen weightsLiteRThttps://llmobile.news/ticker/mobilellm-flash/https://llmobile.news/ticker/mobilellm-flash/Mon, 16 Mar 2026 23:10:00 +0100Meta AI published MobileLLM-Flash on March 16, 2026, three models of 350M, 650M and 1.4B parameters whose shapes were picked by measuring how fast each candidate actually ran on a phone. Meta reports the family reaching up to 1.8 times faster prefill and 1.6 times faster decode than Liquid AI’s LFM2 models on mobile CPUs, at comparable or higher accuracy, with support for up to 8k tokens of context. Meta’s first MobileLLM paper argued in 2024 that deep and thin networks win below a billion parameters, and the Flash paper states that such deep-and-thin structures often fail to improve on-device latency. Meta exported 100 architectures to a phone and found parameter count a poor predictor of measured prefill and decode speed, at a Kendall rank correlation of 0.40, with FLOPs little better at 0.46 and 0.55. The 1.4B model therefore runs 16 layers at a width of 2048, against the 30 layers at 1280 of MobileLLM-Pro-1B. Meta ran the latency tests on a Samsung Galaxy S25, a phone with a Snapdragon 8 Elite chipset, an octa-core CPU and 12 GB of memory, exporting each model through ExecuTorch to the XNNPACK CPU backend at 4-bit weights, 8-bit dynamic activations and a quantised KV cache, on 4 CPU threads and averaged over three runs after a warmup. At a 2k context the 650M model reaches its first token in 3.34 s against 6.01 s for LFM2 700M, and decodes at 85.35 tok/s against 53.57 tok/s. Meta repeated the 1k-context test on an iPhone 17, where the 650M model takes 2.81 s to the first token against 3.40 s for LFM2 700M. Most of the speed comes from letting some layers skip attention, so a token passes through those blocks without being compared to every earlier token. The 350M model keeps full attention in 7 of its 12 layers and the 650M model in 8 of 13, while the 1.4B model attends in all 16. Meta reports that skipping consistently beat sliding window attention, which instead limits each token to a fixed span of recent ones, and that more than three skipped layers in a row costs accuracy at the same latency, dropping TriviaQA exact match from 33.2% to 8.8%. Meta built the three models by pruning a shallow 1.8B variant of MobileLLM-Pro and inheriting its weights, which the paper says took about 35% of the tokens that pretraining MobileLLM-Pro required. Across nine reasoning and knowledge tasks Meta reports averages of 45.46, 48.57 and 55.06 for the three sizes, against 44.92, 48.52 and 50.48 for LFM2 at 350M, 700M and 1.2B, 51.79 for Gemma 3 1B and 49.01 for Llama 3.2 1B. After instruction tuning, the 1.4B model scores 47.89 on MMLU and 46.34 on HumanEval, against 49.30 and 37.80 for Llama 3.2 1B. The paper announces no release of the weights. Chart: Meta. Bubble area shows parameter count, latency measured on a Samsung Galaxy S25. Source: https://arxiv.org/abs/2603.15954 Read the article: https://llmobile.news/ticker/mobilellm-flash/MetaResearchBenchmarksAndroidPyTorchhttps://llmobile.news/ticker/qwen-3-5/https://llmobile.news/ticker/qwen-3-5/Mon, 02 Mar 2026 10:00:00 +0100Alibaba’s Qwen team released Qwen3.5-0.8B and Qwen3.5-2B on March 2, 2026, together with 4B and 9B versions, according to the Qwen release notes. Both are dense vision-language models with a 262,144-token context and Apache 2.0 weights. The model card states that the released files work with Hugging Face Transformers, vLLM, SGLang and KTransformers, and that in light of the parameter scale the intended uses are prototyping, task-specific fine-tuning and other research or development purposes. The benchmark table in that model card puts Qwen3.5-2B at 55.3 on MMLU-Pro with reasoning switched off, where the same table gives 40.2 for last generation’s Qwen3-1.7B and 69.6 for the larger Qwen3-4B-2507, and Qwen3.5-0.8B at 29.7. With reasoning on, Alibaba reports 66.5 for the 2B and 42.3 for the 0.8B on the same test, and 51.6 against 11.9 on GPQA, a set of graduate-level science questions. The same table has Qwen3-1.7B ahead of both new sizes on IFEval, which checks whether a model obeys explicit formatting instructions, at 68.2 against 61.2 for the 2B and 52.1 for the 0.8B with reasoning off. Alibaba ran all of these figures itself. Both sizes ship a vision encoder in the same checkpoint rather than as a separate release, and Alibaba’s vision table gives Qwen3.5-2B 64.2 on MMMU, a college-level image question set, above the 61.4 it lists for the previous Qwen3-VL-2B, with 49 for Qwen3.5-0.8B. The two models stack 24 layers in which three linear-attention blocks, whose memory cost does not grow with the sequence, alternate with one full-attention block. Input embedding and output layer are tied, so a single 248,320-entry matrix does both jobs, and Alibaba puts language coverage at 201 languages and dialects. Alibaba published the weights on Hugging Face and ModelScope under Apache 2.0, and its MNN team put 4-bit exports of both sizes up the same day. The 0.8B build carries a 470 MB language weight file plus 63 MB for the vision encoder, and the 2B build 1.18 GB plus 196 MB, against 1.75 GB and 4.55 GB for the full-precision checkpoints. For other local runtimes the Qwen repository points readers to llama.cpp, which it says supports the series for text and vision, and to mlx-lm and mlx-vlm on Apple silicon, without Alibaba publishing GGUF or MLX builds of these sizes itself. Source: https://huggingface.co/Qwen/Qwen3.5-2B Read the article: https://llmobile.news/ticker/qwen-3-5/AlibabaQwenOpen weightsQuantisationBenchmarkshttps://llmobile.news/ticker/mobile-agent-v3-5/https://llmobile.news/ticker/mobile-agent-v3-5/Sun, 15 Feb 2026 02:52:00 +0100Alibaba’s Tongyi Lab published Mobile-Agent-v3.5 on February 15, 2026, describing GUI-Owl-1.5, a family of agent models that read a screenshot and emit the next tap, type or scroll. The family spans 2B, 4B, 8B, 32B and 235B-A22B parameters in instruct and reasoning variants, all built on Qwen3-VL. Every benchmark in the paper was run server-side against cloud sandboxes and emulators, and the authors give no on-device deployment, latency or hardware figures anywhere, so the small sizes are the only thing that points at a phone. The authors write that the smaller instruct variants, which skip the reasoning step, are fast enough to be deployed on edge devices and to work alongside a larger cloud model. Across platforms, the authors report their 32B-Instruct model reaching 56.5 on OSWorld-Verified for desktop tasks, 47.6 on OSWorld-MCP and 46.8 on Mobile-World, the last two of which also require calling external tools. On AndroidWorld, which scores an agent driving real Android apps in an emulator, their 8B reasoning model reaches 71.6, which the paper puts just behind UI-TARS-2 at 73.3. For browsers, the paper’s table has the 32B reasoning model at 48.4 on WebArena, 46.6 on VisualWebArena and 82.1 on WebVoyager. The small checkpoints carry a large part of that. The authors report 2B-Instruct at 43.5 on OSWorld-Verified and 67.9 on AndroidWorld, and 4B-Instruct at 48.2 and 69.8, which puts the 4B model within 2 points of their own 32B on Android tasks. On ScreenSpot-Pro, which tests whether a model can point at the right element in a high-resolution screenshot, they report 72.9 for 32B-Instruct and 80.3 when the model first crops a region and looks again. The paper’s prose and its tables disagree on which variant owns several headline numbers, and the figures above follow the tables. Charts: Alibaba Tongyi Lab, the authors' own benchmark figures. Six checkpoints are on Hugging Face under the MIT license, covering 2B, 4B and 8B instruct, 8B reasoning, and 32B in both variants. The 235B-A22B model named in the paper is not among them. The code is MIT as well, and the repository points at a hosted cloud-sandbox demo rather than a local runtime. Source: https://arxiv.org/abs/2602.16855 Read the article: https://llmobile.news/ticker/mobile-agent-v3-5/AlibabaAgentsQwenOpen weightsAndroidhttps://llmobile.news/ticker/nemotron-flash/https://llmobile.news/ticker/nemotron-flash/Mon, 24 Nov 2025 09:46:00 +0100Nvidia published Nemotron-Flash on November 24, 2025, a family of small language models built around measured decoding latency instead of parameter count. Nvidia reports Nemotron-Flash-1B decoding 8k tokens in 14.45 seconds on a single Nvidia H100 at batch size 1, against 27.55 seconds for Qwen3-0.6B, while averaging 49.63 across 16 benchmarks to Qwen3-0.6B’s 44.11. The paper was accepted at NeurIPS 2025. The models mix three kinds of layer rather than using attention throughout. Nvidia interleaves DeltaNet and Mamba2 layers, operators that carry a fixed-size state instead of a cache that grows with the context, with a small number of ordinary attention layers, and an evolutionary search picked which operator goes where. Nemotron-Flash-1B holds 0.96B parameters in 12 blocks with only 2 full attention layers, and Nemotron-Flash-3B holds 2.7B parameters in 18 blocks with 3. Nvidia built both wider and shallower than other models of the same size, where Qwen3-0.6B stacks 28 layers, and writes that deep and thin models are parameter efficient but sit off the accuracy-latency frontier. Nemotron-Flash-3B averages 60.98 across the same 16 tasks, which cover MMLU, commonsense reasoning, math, coding and recall, against 58.96 for Qwen2.5-3B and 55.47 for Qwen3-1.7B. On the same H100, with all models running through TensorRT-LLM AutoDeploy kernels and CUDA Graph, Nvidia measured 28.71 seconds for the 3B against 49.40 for Qwen2.5-3B and 36.20 for Qwen3-1.7B, so 1.7 and 1.3 times lower latency. Throughput was measured separately, with a 32k-token input at the largest batch size that still fits in memory, and Nvidia reports 2939 tok/s for the 3B against 459 and 157 for the two Qwen models, and 7289 tok/s for the 1B against 160 for Qwen3-0.6B. Every figure here is Nvidia’s own, taken on data centre GPUs, and the paper gives no phone or embedded measurements, with the earlier depth and operator studies run on an Nvidia A100. Nvidia trained both models on 4.5T tokens using 256 H100 GPUs and released three checkpoints on Hugging Face, Nemotron-Flash-1B, the 3B base model and an instruction-tuned Nemotron-Flash-3B-Instruct, which averages 52.84 across MMLU, GPQA, GSM8K and IFEval against 48.17 for Qwen2.5-1.5B. The model cards set the licence to Creative Commons Attribution-NonCommercial 4.0, so the weights are downloadable but not licensed for commercial use, and neither Apache nor an Nvidia open model licence applies. Running them needs the TensorRT-LLM AutoDeploy attention kernel alongside the Mamba2 and FlashLinearAttention implementations, since Nvidia states that vLLM and TensorRT-LLM do not support DeltaNet-style linear attention on their own. Chart: Fu et al., Figure 1 of the paper. Nvidia's own measurements on an Nvidia H100. Source: https://arxiv.org/abs/2511.18890 Read the article: https://llmobile.news/ticker/nemotron-flash/NvidiaResearchBenchmarksOpen weightshttps://llmobile.news/ticker/mobilellm-pro/https://llmobile.news/ticker/mobilellm-pro/Mon, 10 Nov 2025 06:28:00 +0100Meta Reality Labs published the MobileLLM-Pro technical report on November 10, 2025, describing a 1.08B-parameter language model built for phones and wearables. The model handles a context window of 128,000 tokens, four times what Gemma 3 1B and Qwen 3 0.6B accept. Quantising weights and embeddings to 4 bits takes the file from 2.2 GB in bf16 down to 590 MB for the CPU build and 720 MB for the accelerator build, which cannot share embedding weights. Meta exported the model through ExecuTorch and measured it on a Samsung Galaxy S25 CPU with the XNNPACK backend and on a Samsung Galaxy S24 Hexagon Tensor Processor. On a 2,000-token prompt the paper reports 8.9 s of prefill on the CPU against 2.0 s on the Hexagon unit, and decode speeds of 33.6 tok/s and 31.6 tok/s while generating 1,000 tokens. At 8,000 tokens prefill climbs to 63.5 s on the CPU and 9.8 s on the Hexagon unit, decode falls to 19.7 and 22.8 tok/s, and the key-value cache grows from 14 MB to 40 MB. The architecture puts 30 layers of width 1280 behind 20 attention heads and 4 key-value heads, and shares one weight matrix between the input embedding and the output head, which the authors say saves 260M parameters, about a quarter of the model. Three out of every four layers use local attention, comparing each token only against the 512 most recent ones, with a global layer every fourth one and at both ends. Meta’s model card credits that interleaving with cutting the key-value cache at 8,000 tokens from 117 MB to 40 MB and with 1.8 times lower prefill latency than global attention in every layer. Meta trained the model by distilling the logits of Llama 4 Scout under a KL divergence loss across three pretraining phases on roughly 1.64T tokens of open data, the second of them a step the authors call implicit positional distillation, which stretches the context window to 128k by having the teacher pass on the ability rather than feeding the student long documents. Meta puts the base model 5.7 and 7.9 points above Gemma 3 1B and Llama 3.2 1B on average across the benchmarks its model card lists, and the paper’s table has it at 76.24 on BoolQ against 63.20 and 62.51, at 52.62 on ARC-Challenge against 38.40 and 38.28, and at 100% needle-in-a-haystack retrieval against 96.80 for Llama 3.2 1B. The instruction-tuned version scores 59.8 on HumanEval against 41.5 and 37.8, 44.8 on MMLU against Gemma’s 29.9 and Llama’s 49.3, and 62.0 on IFEval against Gemma’s 80.2. In Meta’s own human evaluation over 100 prompts per task, raters preferred Gemma 3 1B for summarisation 51 to 47 and for rewriting 49 to 45, while MobileLLM-Pro led on recall 47 to 30. Meta released four checkpoints on Hugging Face, the base model, the instruction-tuned one, and two quantisation-ready base checkpoints produced by quantisation-aware training with self-distillation from the full-precision model. One uses 4-bit group-wise weights at group size 32 with 8-bit dynamic activations and an 8-bit key-value cache for CPU backends, the other 4-bit channel-wise weights for the Apple Neural Engine and Qualcomm’s Hexagon processor, which handle group-wise quantisation poorly or not at all. Needle-in-a-haystack retrieval holds at 96.44% and 98.67% against 100% at full precision. The weights sit behind a request form and carry the FAIR Noncommercial Research License. Source: https://arxiv.org/abs/2511.06719 Read the article: https://llmobile.news/ticker/mobilellm-pro/MetaResearchDistillationQuantisationAndroidhttps://llmobile.news/ticker/mobilellm-r1/https://llmobile.news/ticker/mobilellm-r1/Fri, 12 Sep 2025 12:00:00 +0200Meta released MobileLLM-R1 on September 12, 2025, a family of reasoning models at 140M, 360M and 950M parameters that write out a chain of intermediate steps before giving an answer instead of replying directly. Meta reports the 950M model scoring 74.0 on the MATH500 maths benchmark and 19.9 on LiveCodeBench v6, against 73.0 and 14.9 for Qwen3-0.6B. The technical report followed on September 29, 2025. The three models saw 4.2T training tokens, which Meta puts at 11.7% of the 36T tokens behind Qwen3-0.6B. Pre-training ran in two phases of 2T tokens each, resampled from a curated pool of roughly 2T tokens of open datasets, followed by two mid-training phases of 100B tokens that distil from Llama-3.1-8B-Instruct as a teacher, then supervised fine-tuning on reasoning traces. Meta uses the result to argue that reasoning does not require the corpora above 10T tokens that recent models have been trained on. For the post-trained models Meta’s table puts MobileLLM-R1-950M at 74.0 on MATH500, 67.5 on GSM8K, 15.5 on AIME 2024 and 19.9 on LiveCodeBench v6, where Qwen3-0.6B reaches 73.0, 79.2, 11.3 and 14.9 and DeepSeek-R1-Distill-Qwen-1.5B reaches 83.2, 77.3, 29.1 and 19.9. Among the models whose weights, data and recipe are all public, the same AIME figure compares with 0.6 for OLMo-2 1.48B and 0.3 for SmolLM2-1.7B. On code the 950M base model scores 46.3 on HumanEval against 30.5 for Qwen3-0.6B-Base, and the 140M base model reaches 15.9 where SmolLM2-135M scores 0.0. Meta profiled the models through ExecuTorch on a Samsung Galaxy S22 with 8 GB of RAM, quantised to 4-bit weights with 8-bit dynamic activations. The 140M model generated 129.67 tok/s at a 1k context and 79.71 tok/s at 32k, the 950M model 31.05 tok/s at 1k before running out of memory at 16k, and Llama-3.2-1B 28.71 tok/s at 1k with the same memory failure. The architecture follows Meta’s earlier MobileLLM work and Llama 3.2, sharing one weight matrix between the input embedding and the output layer, and the post-trained models take a 32k context. Meta released the weights on Hugging Face together with the training code, the data sources and the data mixing ratios on GitHub, under the FAIR Noncommercial Research License, which covers research, education and analysis but not commercial use. Access to the model repositories is gated behind a form. Update, November 24, 2025. Meta released MobileLLM-R1.5 at the same three sizes, fine-tuned from the R1 models with one further round of on-policy distillation, where the student answers first and the teacher, here Llama-3.1-Nemotron-Nano-4B-v1.1, grades its own words back to it token by token. Meta reports the 950M version at 86.6 on MATH500, 82.6 on GSM8K, 39.9 on AIME 2024 and 29.1 on LiveCodeBench v6, up from 74.0, 67.5, 15.5 and 19.9 for MobileLLM-R1-950M, and says it beats DeepSeek-R1-Distill-Qwen-1.5B on every maths and coding benchmark it ran. At 360M Meta puts MATH500 at 63.4 against 28.4 and GSM8K at 52.8 against 24.5. Source: https://arxiv.org/abs/2509.24945 Read the article: https://llmobile.news/ticker/mobilellm-r1/MetaResearchBenchmarksDistillationAndroidhttps://llmobile.news/ticker/demystifying-slms-edge/https://llmobile.news/ticker/demystifying-slms-edge/Sun, 27 Jul 2025 09:00:00 +0200Ten researchers at Beijing University of Posts and Telecommunications, Peng Cheng Laboratory, the University of Cambridge, Xiaomi, Helixon Research and Flower Labs collected 68 small language models between 100M and 5B parameters, released by 24 organisations between OPT in May 2022 and Llama 3.2 in September 2024, and measured both their accuracy and their cost on edge hardware. In Demystifying Small Language Models for Edge Deployment, presented at ACL 2025, Microsoft’s Phi-3 and Phi-3.5-mini sit highest on the capability chart at about 70 percent average accuracy over 10 reasoning and knowledge datasets, level with the Llama 3.1 line the authors draw for comparison. The runtime half of the study covers the 20 models that llama.cpp supports, quantised to 4-bit and run with a 50-token prompt and 50 generated tokens. Chart: Lu et al., licensed CC BY 4.0. Accuracy is averaged over the 10 datasets with math excluded, and circle size is proportional to model size. Parameter count predicts on-device latency poorly. The measurements put OpenELM-3B at 9.4 percent more parameters than Phi-2 but 9.9 percent faster to the first token, and Qwen1.5-0.5B at 25.4 percent more parameters than Qwen2-0.5B yet 31.9 percent faster to the first token and 11.8 percent faster per decoded token. Qwen1.5-1.8B carries 30.5 percent more parameters than Pythia-1.4B and still reaches the first token 18.5 percent sooner. The authors trace the gap to how a model spends its parameter budget, with Qwen2 sharing its input embedding and output layer and putting the saved weights into wider attention and feed-forward layers, which costs compute in the prefill stage. Memory ran from 275 MB to 2456 MB across the 20 models, measured with llama.cpp on a Jetson Orin NX at a fixed 2048-token context. Vocabulary size moves that figure as much as parameter count does, and the paper reports Bloom-560M needing 492 MB of compute buffer for its 250,880-token vocabulary, 3.5 times what OpenELM-1.1B needs with 32,000 tokens. OpenELM-1.1B uses 23.8 percent less memory than Bloom-560M despite 32.3 percent more parameters, and OpenELM-3B 13.8 percent less than Gemma-2B despite 21.1 percent more, which the authors put down to the small vocabulary and to grouped-query attention, where several attention heads share one set of cached keys and values. Raising each model to its own maximum context window pushes Qwen2-0.5B past 6 GB, with compute buffer and KV cache making up 85 percent of the total for the Qwen2 models. Charts: Lu et al., licensed CC BY 4.0. Measured with llama.cpp at 4-bit, and memory taken on a Jetson Orin NX at a 2048-token context. Quantisation paid off in decoding rather than in prefill. Testing five settings on Phi-1.5, the authors found Q4_K_M the strongest, roughly halving per-token decode latency against FP16, while at a 50-token prompt it cut the time to the first token by only 13 percent. Q6_K and Q3_K matched or exceeded FP16 on longer prompts, which the paper puts down to the alignment and padding their irregular bit widths force. The hardware gap was wider than the quantisation gap, with the Jetson’s GPU 40 times faster than the Meizu 18 Pro’s CPU at prefill but only 1.84 times faster at decode, and the Xiaomi 12S on a Snapdragon 8 Gen 1+ the fastest of the three phones, ahead of the Pixel 7 Pro and the Meizu on its Snapdragon 888. The authors name two limits on their own work. They left math datasets out of the capability evaluation because of the gap between small models and larger ones on mathematical reasoning, and they confined the cost analysis to the 20 models llama.cpp supports so that the inference engine stayed constant, excluding every model it cannot load. The paper is free to read at the ACL Anthology under a Creative Commons Attribution 4.0 licence, and the same group had earlier measured 22 models from 0.5B to 7B on four Android phones, three of which reappear in this test bed. PalmBench adds power draw and surface temperature on comparable hardware, and Artificial Analysis has since reported quality, speed and memory together for 33 quantised models on one iPhone 17 Pro. Source: https://aclanthology.org/2025.acl-long.718/ Read the article: https://llmobile.news/ticker/demystifying-slms-edge/BenchmarksResearchllama.cppMemoryAndroidhttps://llmobile.news/ticker/apple-foundation-models-report-2025/https://llmobile.news/ticker/apple-foundation-models-report-2025/Fri, 18 Jul 2025 01:37:00 +0200Apple posted its 2025 foundation models tech report to arXiv on July 17, 2025, giving the numbers behind the roughly 3-billion-parameter on-device model it shipped at WWDC25. The report’s Table 3 puts the price of compressing that model to 2 bits per weight at 3.4 points of MMLU, from 67.8 in 16 bits down to 64.4, with IFEval falling from 85.1 to 82.3. Apple reaches 2 bits through quantisation-aware training with a learnable scaling factor per weight tensor, and reports that a balanced set of four levels at -1.5, -0.5, 0.5 and 1.5 trains more smoothly than the usual -2, -1, 0 and 1. The embedding table is quantised to 4 bits and the key-value cache to 8 bits, and low-rank adapters are fine-tuned afterwards to claw back the quality the compression costs. The server model takes a different route, compressed after training to 3.56 bits per weight with Adaptive Scalable Texture Compression, a GPU texture format whose fixed-function decode hardware on Apple GPUs unpacks the weights at no cost to the compute cores. The report also spells out the KV cache sharing the framework announcement only named. Apple splits the transformer so that Block 1 holds 62.5 percent of the layers and Block 2 the remaining 37.5 percent with its key and value projections removed, reusing Block 1’s cache instead. That cuts KV cache memory by 37.5 percent, and because Block 2 produces no keys or values, prefill skips its computation entirely, which Apple says cuts time to first token by about the same 37.5 percent. Apple rebuilt how the on-device model is trained. The company first trained a dense model on about 14T tokens, sparse-upcycled it into a 64-expert mixture-of-experts version, where only part of the model runs per token, using 1T high-quality tokens, then retrained the dense model on its last 10 percent of tokens against a distillation loss from that MoE teacher. Apple states this cut the cost of training the teacher by 90 percent and removed the need for structural pruning. The server model was trained on 8192 v5p Cloud TPU chips for 13.4T tokens. For the language expansion the report states the models are designed to support 16 languages, built on a text tokenizer grown from a 100k to a 150k vocabulary and a continued pre-training mix whose multilingual share rose from 8 percent to 30 percent. In Apple’s own human evaluation on US English text prompts, graders preferred the on-device model over Qwen-2.5-3B on 35.3 percent of prompts against 12.9 percent the other way, while against Gemma-3-4B the split was 21.0 percent to 21.9 percent. The report is on arXiv under a Creative Commons Attribution 4.0 license. Chart: Apple's own human evaluation, from the tech report. Rows marked with an asterisk were tested against the compressed on-device model. Source: https://arxiv.org/abs/2507.13575 Read the article: https://llmobile.news/ticker/apple-foundation-models-report-2025/AppleApple SiliconQuantisationDistillationBenchmarkshttps://llmobile.news/ticker/lfm2/https://llmobile.news/ticker/lfm2/Thu, 10 Jul 2025 14:00:00 +0200Liquid AI released LFM2 on July 10, 2025, three open-weight checkpoints of 350M, 700M and 1.2B parameters aimed at the CPUs inside phones, laptops and vehicles. The company reports 2x faster decode and prefill speed on CPU than Qwen3, and describes the models as built for millisecond latency and offline operation on the device rather than in a data centre. The architecture is a hybrid of 16 blocks, 10 of them gated short convolutions and 6 grouped query attention. A short convolution mixes each token with a handful of its neighbours instead of attending over the whole history, which is cheap on a phone CPU and keeps the attention cache small, while the six attention blocks preserve the recall that pure convolutions lose. Liquid AI says the layout came out of its STAR architecture search, which it ran against measured peak memory and measured prefill plus decode speed on Qualcomm Snapdragon embedded SoC CPUs rather than against proxy metrics. Liquid AI benchmarked the models on a Samsung Galaxy S24 Ultra with a Qualcomm Snapdragon SoC and on an AMD Ryzen HX370 laptop, using Q4_0 in llama.cpp and 8da4w in ExecuTorch. In its llama.cpp charts LFM2-350M clears 1,300 tokens per second of prefill and runs at roughly 190 tokens per second of decode on the Galaxy S24 at a 128-token sequence, falling to around 120 tokens per second of decode at 4,096 tokens. The company states that LFM2-700M stays faster than Qwen3-0.6B on both decode and prefill in both runtimes despite being 16 percent larger, and that the CPU gains should carry over to GPUs and NPUs once kernels are optimised. Charts: Liquid AI's own llama.cpp measurements at Q4_0 quantisation. The models are small enough to ship whole, with Liquid AI’s own Q4_0 GGUF builds at 219 MB for LFM2-350M and 696 MB for LFM2-1.2B. On Liquid AI’s internal evaluation table LFM2-1.2B scores 55.23 on MMLU and 74.89 on IFEval against 59.11 and 73.98 for Qwen3-1.7B, a model with 47 percent more parameters, while LFM2-700M reaches 49.9 on MMLU against 40.08 for Gemma 3 1B IT. Liquid AI notes that it evaluated Qwen3 in non-reasoning mode only, on the grounds that reasoning traces run past the output budgets that matter on edge devices. Chart: Liquid AI. The average covers MMLU, IFEval, IFBench, GSM8K and MMMLU. Weights are on Hugging Face, with the models also reachable through the Liquid Playground and OpenRouter, and Liquid AI points developers at llama.cpp and ExecuTorch for local testing. The licence is an open licence based on Apache 2.0 that permits academic and research use freely and commercial use by companies under $10 million in revenue, with anyone above that threshold directed to contact the company for a commercial licence. All three checkpoints were trained on 10T tokens, roughly 75 percent English, 20 percent multilingual and 5 percent code, with the context extended to 32K during pre-training. Update, September 23, 2025. Liquid AI added LFM2-2.6B, which widens the stack to 30 blocks, 22 of them convolutions and 8 attention. The company reports 82.41 percent on GSM8K and 79.56 percent on IFEval, and says the model outperforms Llama 3.2-3B-Instruct, Gemma 3 4B IT and SmolLM3-3B. It ships on Hugging Face under the same licence. Update, November 28, 2025. Liquid AI published an LFM2 technical report covering the dense 350M to 2.6B models plus an 8.3B mixture-of-experts variant with 1.5B active parameters, where only part of the model runs per token. Measuring on a Samsung Galaxy S25 with a Snapdragon 8 Elite in llama.cpp at Q4_0, it reports LFM2-350M at 1,067 tokens per second of prefill and 194.1 tokens per second of decode on a 1K-token prompt, and LFM2-2.6B at 143 and 33.8 against 57 and 17.2 for Qwen3-4B. The report names ExecuTorch, llama.cpp and vLLM as the shipped deployment packages. Source: https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models Read the article: https://llmobile.news/ticker/lfm2/Liquid AILFMOpen weightsllama.cppBenchmarkshttps://llmobile.news/ticker/smollm3/https://llmobile.news/ticker/smollm3/Tue, 08 Jul 2025 12:30:00 +0200Hugging Face released SmolLM3 on July 8, 2025, a 3B model trained on 11.2 trillion tokens that takes up to 128k tokens of context, covers six languages and answers in either of two modes. It is the successor to SmolLM2, whose largest model was 1.7B with an 8k context window and, in Hugging Face’s own words, primarily understands and generates content in English. The longer context comes from an architecture change plus a separate training stage. Hugging Face removed rotary position embeddings from every fourth layer, a technique it calls NoPE, and replaced multi-head attention with grouped-query attention to shrink the key-value cache during inference. After the main pretraining run it trained the model on another 100 billion tokens in two steps, 4k to 32k and then 32k to 64k, and YaRN, a method for extrapolating past the trained length, carries it to 128k tokens at inference, though the published configuration ships set to 64k and has to be edited to go further. Multilingual text made up 12 percent of the web data at every pretraining stage, and Hugging Face lists English, French, Spanish, German, Italian and Portuguese as supported. Hugging Face’s own base-model figures put SmolLM3 at 76.2 on HellaSwag, 65.6 on ARC and 79.0 on BoolQ, ahead of both Llama 3.2 3B and Qwen2.5 3B and ahead of the larger Qwen3 4B on all three. Qwen3 4B stays in front on maths and code, at 51.2 against 46.1 on MATH and 54.9 against 30.5 on HumanEval+, and on the Ruler 64k long-context test Llama 3.2 3B leads at 72.9 against 67.9 for SmolLM3. On the Flores-200 translation benchmark SmolLM3 scores 56.3 against 55.8 for Qwen3 4B and 54.9 for Qwen2.5 3B, and its multilingual chart shows it ahead of both 3B rivals in all five non-English languages and behind Qwen3 4B in all five. Table: Hugging Face's own base-model figures. A flag in the system prompt, /think or /no_think, decides whether the instruct model reasons before it answers. Hugging Face reports the reasoning mode at 36.7 percent on the AIME 2025 maths competition against 9.3 percent without it, 41.7 against 35.7 on GPQA Diamond and 30.5 against 15.2 on LiveCodeBench v4. Qwen3 4B leads on most of the eight tests in that comparison in both modes, at 58.8 on AIME 2025 and 52.9 on LiveCodeBench, though SmolLM3 without reasoning tops it on the IFEval instruction-following test at 76.7 to 68.9. Table: Hugging Face's own instruct-model figures. Hugging Face published the recipe as well as the weights. Pretraining ran in three stages that moved web data from 85 percent to 75 and then 63 percent while code rose from 12 percent to 24 and maths from 3 percent to 13, each share drawn from named public datasets, and the company released the training configs with the exact data weights along with intermediate checkpoints. Both the base and instruct models are on Hugging Face under the Apache 2.0 license, and the model card names llama.cpp, ONNX, MLX, MLC and ExecuTorch for local inference. The 4-bit GGUF published by ggml-org on release day is 1.92 GB, against 3.27 GB for the 8-bit build for MLX and 6.15 GB for the bfloat16 weights. Diagram: Hugging Face. Source: https://huggingface.co/blog/smollm3 Read the article: https://llmobile.news/ticker/smollm3/Hugging FaceOpen weightsBenchmarksOpen sourcellama.cpphttps://llmobile.news/ticker/gemma-3n/https://llmobile.news/ticker/gemma-3n/Tue, 20 May 2025 19:00:00 +0200Google previewed Gemma 3n on May 20, 2025, a model built for phones, tablets and laptops. Its 5B and 8B versions run with dynamic memory footprints of 2 GB and 3 GB, which Google says matches what 2B and 4B models normally need. The saving comes from Per-Layer Embeddings, a Google DeepMind technique that keeps embedding parameters off the accelerator. For E2B that cuts what has to be loaded into accelerator memory from 5.44B parameters to 1.91B, with the 2.55B embedding parameters cached to fast storage instead. A MatFormer setup nests a smaller but fully functional 2B model inside the 4B one, so developers can trade quality against footprint. Google reports the model starting to respond about 1.5 times faster on mobile than Gemma 3 4B. Gemma 3n takes audio, text, images and video as input, including interleaved inputs across modalities, and handles speech recognition and translation. Google states that the same architecture powers the next generation of Gemini Nano, which was to reach Android and Chrome later in 2025. Qualcomm Technologies, MediaTek and Samsung System LSI worked on the optimisation. Diagram: Google. ▶Announcing Gemma 3n Preview: Powerful, Efficient, Mobile-First AILoading connects your browser to www.youtube-nocookie.com, which may process your IP address and use cookies. Load contentOpen externallyVideo: Google for Developers. Update, June 26, 2025. Google released Gemma 3n as E2B and E4B, naming them for 2B and 4B effective parameters out of 5B and 8B raw ones. Google reports E4B scoring 1303 on LMArena and calls it the first model under 10 billion parameters to pass 1300, ahead of Llama 4 Maverick 17B 128E at 1292 and GPT 4.1-nano at 1288. The release adds a MobileNet-V5-300M vision encoder, which Google says handles up to 60 frames per second on a Pixel, and KV cache sharing that doubles prefill performance. Text covers 140 languages, multimodal understanding 35. Weights are on Hugging Face and Kaggle, with support in Ollama, llama.cpp, MLX, LiteRT and the Google AI Edge Gallery. Chart: Google. Source: https://developers.googleblog.com/en/introducing-gemma-3n/ Read the article: https://llmobile.news/ticker/gemma-3n/GoogleGemmaGemini NanoAndroidOpen weightshttps://llmobile.news/ticker/qwen-3/https://llmobile.news/ticker/qwen-3/Wed, 14 May 2025 15:41:00 +0200Alibaba’s Qwen Team published the Qwen3 Technical Report on May 14, 2025, covering six dense models from 0.6B to 32B parameters and two mixture-of-experts models, where only part of the model is active per token. The two smallest dense sizes, Qwen3-0.6B and Qwen3-1.7B, carry the same mode switch as the rest of the family, so one set of weights either works through a problem step by step before answering or replies straight away. Alibaba reports Qwen3-1.7B scoring 93.4 on MATH-500, a 500-problem maths set, with reasoning on, against 73.0 with it off. Earlier Qwen releases needed two separate models for that, a chat model and a dedicated reasoning model such as QwQ-32B. In Qwen3 the user picks the behaviour with a flag in the prompt or the system message, /think or /no_think, and the report states that reasoning is the default and that across a multi-turn conversation the model follows the last flag it saw. The same section describes a reasoning budget, where the model’s working is cut off once it passes a token limit the user set and it answers from what it has so far, which the authors say they never trained for and which emerged from merging the two modes. The benchmark tables give both small sizes in both modes. For Qwen3-0.6B, Alibaba reports 55.6 on MMLU-Redux, a corrected version of the MMLU knowledge test, with reasoning on against 44.6 with it off, and 77.6 against 55.2 on MATH-500. For Qwen3-1.7B the same pair of tables gives 73.9 and 64.4 on MMLU-Redux, 48.3 against 13.4 on the AIME 2024 competition maths set, and 33.2 against 11.6 on LiveCodeBench v5. Against the reasoning baselines in the report, Qwen3-0.6B is ahead of DeepSeek-R1-Distill-Qwen-1.5B on MMLU-Redux, 55.6 to 45.4, and behind it on MATH-500, 77.6 to 83.9. Alibaba built the small sizes by copying behaviour from the large ones rather than by running the full reinforcement learning pipeline on each. The report describes two stages of distillation from Qwen3-32B and Qwen3-235B-A22B, first on stored teacher outputs generated in both modes, then by matching the small model’s output probabilities against the teacher’s while the small model generates, and it credits the first stage with teaching the smaller models to switch modes at all. Pre-training ran on about 36 trillion tokens covering 119 languages and dialects, up from 29 in Qwen2.5. Context length is where the two small sizes sit apart from the family. Table 1 of the report puts Qwen3-0.6B and Qwen3-1.7B at 32K tokens against 128K for Qwen3-8B and every larger model, and both tie the input embedding to the output layer so one weight matrix does both jobs. All eight models are published under the Apache 2.0 license on Hugging Face, ModelScope and Kaggle. The Qwen3 blog post of April 29, 2025 names Ollama, LM Studio, MLX, llama.cpp and KTransformers as the tools it recommends for running them locally. Source: https://arxiv.org/abs/2505.09388 Read the article: https://llmobile.news/ticker/qwen-3/AlibabaQwenOpen weightsResearchBenchmarkshttps://llmobile.news/ticker/gemma-3/https://llmobile.news/ticker/gemma-3/Wed, 12 Mar 2025 17:00:00 +0100Google released Gemma 3 on March 12, 2025 in four sizes, 1B, 4B, 12B and 27B, and describes them as designed to run fast directly on devices, from phones and laptops to workstations. The 1B is a new size for the line, which started at 2B with Gemma and stayed there through Gemma 2. The technical report puts the 1B at 2.0 GB of weights in bfloat16 and at 0.5 GB as a quantised int4 checkpoint, and the 4B at 8.0 GB and 2.6 GB. Context length is not the same across the four. The report gives 128K tokens for the 4B, 12B and 27B and 32K for the 1B, with the three larger models pre-trained at 32K and stretched to 128K at the end of pre-training by rescaling their rotary position embeddings. To stop the key-value cache from swallowing memory at long context, Gemma 3 places five local attention layers between each global one and holds the local layers to a 1024-token window, where Gemma 2 alternated one for one. Google reports that in an ablation on a 2B model a global-attention-only configuration carries a 60 percent memory overhead from the cache at a 32K prefill, and that interleaving with a 1024-token window brings that under 15 percent. Multimodality starts at 4B. The 4B, 12B and 27B share one 400M-parameter SigLIP vision encoder, frozen during training, which takes square images resized to 896 by 896 pixels and condenses each one into 256 tokens for the language model to read. An inference-time step Google calls Pan and Scan cuts non-square or high-resolution images into equal crops and resizes each to the encoder’s resolution, which the report measures as raising the 4B from 72.8 to 81.0 on DocVQA and from 44.1 to 57.0 on InfoVQA in a 4-shot evaluation of a pre-trained checkpoint. The 1B has no vision encoder and takes text only. Google published quantisation-aware trained checkpoints next to the raw weights, made by fine-tuning each model for about 5,000 steps against the probabilities of the unquantised model, so the weights are trained to tolerate the lower precision rather than rounded down afterwards. The three formats are per-channel int4, per-block int4 and switched fp8, which Google picked for what open source inference engines such as llama.cpp already support. The report’s footprint table counts weights alone and weights plus a key-value cache for a 32,768-token sequence, and on the second measure the 1B needs 1.4 GB at per-channel int4 against 2.9 GB in bfloat16, while the 4B needs 7.3 GB against 12.7 GB. Google's own footprint figures for the raw and quantised checkpoints, with and without a key-value cache at 32,768 tokens. Table: Google DeepMind, Gemma 3 technical report, CC BY 4.0. Google reports its own zero-shot figures for the instruction-tuned models and puts Gemma 3 4B at 43.6 on MMLU-Pro and 75.6 on MATH, against 15.6 and 27.2 for Gemma 2 2B and 56.9 and 55.6 for Gemma 2 27B, with the 1B at 14.7 and 48.0. The 4B also scores 48.8 on the MMMU validation set, a benchmark the text-only sizes do not run, and the report calls the instruction-tuned 4B competitive with the instruction-tuned Gemma 2 27B. Google put every size on Hugging Face, Kaggle and Ollama as a pre-trained and an instruction-tuned checkpoint under the Gemma Terms of Use, and names LiteRT, Gemma.cpp, llama.cpp and Google AI Edge among the runtimes that take them. The same release added ShieldGemma 2, a 4B image safety classifier built on Gemma 3 that labels images for dangerous content, sexually explicit material and violence. ▶What's new in Gemma 3?Loading connects your browser to www.youtube-nocookie.com, which may process your IP address and use cookies. Load contentOpen externallyVideo: Google for Developers. Source: https://blog.google/technology/developers/gemma-3/ Read the article: https://llmobile.news/ticker/gemma-3/GoogleGemmaQuantisationOpen weightsBenchmarkshttps://llmobile.news/ticker/pareto-q/https://llmobile.news/ticker/pareto-q/Tue, 04 Feb 2025 19:59:00 +0100Meta published ParetoQ on February 4, 2025, a training framework that puts 1-bit, 1.58-bit, 2-bit, 3-bit and 4-bit quantisation through the same recipe so the bit widths can be compared against each other rather than against methods built for one width. The result the authors draw from it is that 1.58-bit, 2-bit and 3-bit land close together and generally beat 4-bit on accuracy against model size, while 1-bit drops away. On LLaMA-3 8B the paper’s table gives an average of 71.2 across five commonsense tasks at 2 bits and 69.0 at 1.58 bits against 74.6 for the full-precision model, then 64.1 at 1 bit. That ranking only holds when size is counted in bytes rather than parameters, which is how a phone’s memory budget works. The authors measure each model by its effective quantised size, every weight counted at its own bit width plus the embeddings at theirs, and compare across that axis. Their example is a 2-bit MobileLLM-1B scoring 61.0 on an eight-task average against 59.2 for a 4-bit MobileLLM-600M, 1.8 points higher at a smaller effective size, and the paper reports the same pattern across LLaMA models up to 8B. The paper also describes where the training behaviour changes. At 3 bits and above the fine-tuned weights stay within 10 to 20 percent of the full-precision weights they started from and recover most of the accuracy after about 10B tokens, while at 2 bits and below they move by roughly 40 percent and need around 30B tokens, which the authors read as the network rebuilding its representations instead of nudging them. On that basis they argue against training ternary models from scratch, the route taken by BitNet b1.58 and Spectra, and put their ternary 600M model at 58.7 on a six-task average, against 57.5 for the ternary 3B model of the 1-bit Era and 58.2 for Spectra’s ternary 3.9B, both computed from the same table. For speed the authors wrote a 2-bit CPU kernel and timed five MobileLLM models on an Apple M1 MacBook Pro with 32 GB of memory using 6 threads, 5 prompt tokens and 122 generated tokens, and report the 2-bit curve sitting above the 4-bit one on accuracy against decoding speed without naming a factor. The one speedup they put a number on is on an H100 NVL GPU, where their 2-bit kernel built on CUTLASS runs 4.14 times faster than FP16 and 1.24 times faster than the 4-bit Machete kernel from vLLM at a 16384 by 16384 weight shape. At 1B, 3B and 8B the authors say the in-kernel conversion overhead cancels that gain and 4-bit gives the better speed-accuracy trade-off. Hardware is also why the paper favours 2-bit over ternary despite the two sitting together on its accuracy-size front. Storing three values needs either sparsity above 90 percent to pay for the indexing or packing that complicates the matrix multiply, and the authors note that some implementations store ternary weights as 2-bit signed integers anyway, which gives up the storage saving. They write that broad adoption will need “community-wide efforts, such as INT2 support in NVIDIA tensor cores”, and the paper itself names no weight release. Update, May 30, 2025. Meta published the ParetoQ checkpoints on Hugging Face, five MobileLLM sizes from 125M to 1.5B, each as a BF16 model plus 1-bit, 1.58-bit, 2-bit, 3-bit and 4-bit versions, 30 repositories in all. They sit behind a gated form under Meta’s FAIR Noncommercial Research License, which permits research use only. The training code had gone up on GitHub on March 3, 2025. Source: https://arxiv.org/abs/2502.02631 Read the article: https://llmobile.news/ticker/pareto-q/MetaResearchQuantisationMemoryBenchmarkshttps://llmobile.news/ticker/slms-can-still-pack-a-punch/https://llmobile.news/ticker/slms-can-still-pack-a-punch/Fri, 03 Jan 2025 20:53:57 +0100Shreyas Subramanian, Vikram Elango and Mecit Gungor of Amazon surveyed about 160 papers on small language models on January 3, 2025, covering general-purpose models under 8B parameters plus a few exceptions up to 13B. Their central claim is that some of these models reach the performance of models 10 to 100 times their parameter count, a ratio the authors call effective size and anchor to PaLM at 540B parameters. In their table, Phi-1 at 1.3B parameters is credited with an effective size of 175B on code benchmarks and Phi-2 at 2.7B with 70B. The survey is clear about where that stops. Its appendix prints completions from 14 models for the prompt “The diagonals of a rectangle”, where a 33M TinyStories model answers “and they were so beautiful” and a 125M GPT Neo model loops on its own words, while TinyLlama at 1.1B and Phi 1.5 at 1.3B give the correct definition. The authors conclude that several billion parameters are the minimum for usable general-purpose responses, and they caution that every effective size they report comes from each paper’s best reported benchmark, so results on other benchmarks can be much lower. The clearest wins they list are task-specific and stay inside one domain, with WizardMath at 7B beating Llama 2 70B on maths benchmarks and Code Llama at 7B beating it on coding benchmarks. On what closes the gap, the survey puts training data first. It credits Phi-1 with 50.6 percent on HumanEval at 1.3B parameters trained on textbook-quality synthetic data, and TinyGSM, where a 1.3B generator paired with a 1.3B verifier scored 81.5 on the GSM8K maths set against 77.4 for the GPT-3.5 model that wrote its training data. Distillation covers the training side, with Orca 2 at 13B leading Llama 2 70B by an average of 3.23 points after learning step-by-step explanations from a teacher model rather than just its answers. After training, the survey groups quantisation and pruning, citing SmoothQuant for 8-bit weights and activations at up to 1.56 times the speed and half the memory, and Sheared-LLaMA for cutting Llama 2 7B down to 1.3B and 2.7B using 3 percent of the compute that training those sizes from scratch would need. The authors name resource-constrained deployment as the main motivation for the whole field, listing mobile devices, edge computing and regions with limited connectivity, and point at SmolLM at 135M, 360M and 1.7B parameters as a family built to run locally on phones and laptops. They note that extreme quantisation and sparse training already put Llama inference on CPUs within reach, and that 8-bit Adam training of a 7B model fits on a single 24 GB Nvidia RTX 4090. The paper is on arXiv under a CC BY-NC-ND 4.0 license, and its authors maintain a leaderboard of small models on Hugging Face Spaces. Source: https://arxiv.org/abs/2501.05465 Read the article: https://llmobile.news/ticker/slms-can-still-pack-a-punch/ResearchBenchmarksDistillationQuantisationPhihttps://llmobile.news/ticker/bluelm-v/https://llmobile.news/ticker/bluelm-v/Sat, 16 Nov 2024 01:14:51 +0100Researchers at CUHK MMLab and vivo AI Lab published BlueLM-V-3B on November 16, 2024, a vision-language model designed together with the code that runs it on a phone. The authors deployed it on the NPU of a MediaTek Dimensity 9300 in a vivo X100 and report a peak memory requirement of 2.2 GB and a generation speed of 24.4 tok/s with the language model weights quantised to 4-bit. The model pairs a 2.7B in-house BlueLM language model with a 400M SigLIP vision encoder that takes 384 by 384 image tiles. The authors report an average score of 66.1 on OpenCompass, a suite of eight multimodal benchmarks, which they state is the highest among models of 4B parameters or fewer, ahead of the 8B models MiniCPM-V-2.6 and InternVL2-8B. Most of the speed comes from how the model decides what resolution to feed the encoder. Dynamic resolution schemes pick a grid of tiles to fit an image, and the authors show that existing choices enlarge pictures a lot, with LLaVA-NeXT resizing and padding a 394 by 390 image to 768 by 768, four times the area, and InternVL 1.5 stretching a 380 by 76 image to 1920 by 384, twenty-five times the area. More pixels mean more tiles and more image tokens, so BlueLM-V-3B uses what the authors call relaxed aspect ratio matching, which tolerates a slightly smaller effective resolution when it wastes less of the canvas. Both examples then land on a single 1:1 tile, and across the 665k-sample LLaVA training set the method picks a smaller grid than InternVL 1.5 in 523k cases. The rest is shaped around what an NPU is bad at. The authors encode image tiles in batches of four at a time, which they measured as the fastest setting, and run the SigLIP convolution layer on the CPU in parallel with the transformer blocks on the NPU to hide 200 ms of latency. A downsampler merges every 2 by 2 patch into one token, cutting 729 tokens per tile to 196, and the prefill runs in chunks of 128 tokens rather than all at once. Precision is mixed, with INT4 for the language model weights, INT8 for the vision encoder and projector weights and for the KV cache, and INT16 and FP16 activations, which the authors say keeps accuracy stable because activations are more sensitive to quantisation than weights. Against the 8B MiniCPM-V 2.5 running on the CPU of the same chip through llama.cpp, the authors report 2.53 s to load and encode a 768 by 1536 image and 2.7 s of prefill, versus 4.0 s and 13.9 s, at 24.4 tok/s against 4.9 tok/s. The paper describes no public release of weights or code, and the authors say future work will target a broader range of mobile devices. Source: https://arxiv.org/abs/2411.10640 Read the article: https://llmobile.news/ticker/bluelm-v/MediaTekNPUQuantisationResearchBenchmarkshttps://llmobile.news/ticker/phonelm/https://llmobile.news/ticker/phonelm/Thu, 07 Nov 2024 03:19:00 +0100Researchers at Beijing University of Posts and Telecommunications published PhoneLM on November 7, 2024, a small language model family in 0.5B and 1.5B sizes built in an unusual order. The team first searched transformer configurations for the fastest inference on a specific phone, then pre-trained the configuration that won, instead of choosing a shape for capability and optimising speed after the fact. PhoneLM-1.5B decodes at 58 tok/s on the CPU of a Xiaomi 14 with a Qualcomm Snapdragon 8 Gen 3. The search varied the layer count from 15 to 25, the ratio of intermediate to hidden size between 2 and 5 and the attention type, and the paper lists measured throughput for seven candidate 1.5B shapes on that phone. The winner has 19 layers and a hidden size of 2,560, a hidden size to layer ratio of 134.7 against the 54.6 to 88.6 the authors observe in recent small models. It uses ReLU rather than the SiLU activation most current small models adopt, which the authors argue is cheaper on NPUs built for integer arithmetic and leaves more zeros in the feed-forward output for sparse-computation tricks. The paper notes that only the 1.5B model went through the search. Across seven zero-shot tasks including HellaSwag, PIQA, BoolQ and both ARC sets, the benchmark table puts PhoneLM-1.5B at an average of 67.31%, ahead of SmolLM-1.7B at 66.49% and StableLM 2 1.6B at 66.34% and behind DCLM-1.4B at 68.33% and Gemma 2 2B at 71.31%. PhoneLM-0.5B averages 60.43% against 60.17% for SmolLM-360M and 56.46% for OpenELM-450M. The authors measure PhoneLM-1.5B decoding 1.2 times faster than StableLM 2 1.6B and 1.6 times faster than SmolLM 1.7B on the same handset. The two models saw 1.1T and 1.5T training tokens, all of them from open datasets such as DCLM-baseline, StarCoderData and Dolma. The group ran every device measurement through its own inference engine mllm, with the linear and embedding weights quantised to 4-bit and four threads pinned to the phone’s performance cores. On the NPU it goes through Qualcomm’s QNN framework, where the authors report prompt processing for PhoneLM-1.5B at 654 tok/s on the Xiaomi 14 against 602 tok/s for Qwen 2.5 1.5B. The 4-bit mllm build the group publishes is a 1.41 GB file. The paper states no peak memory figure. An Android demo app on top of mllm runs PhoneLM-1.5B locally for chat and for Android intent invocation, so a user who asks to be woken at 8 o’clock has an alarm set through the system’s own alarm intent. That variant, PhoneLM-1.5B-Call, was fine-tuned with LoRA on DroidCall, 10,000 synthetic function-calling examples covering alarms, timers, email drafts and searches, and the paper places it above GPT-4o-mini, which scores 71.0% on the same test set. Weights are on Hugging Face under Apache 2.0 and the data preparation, training, fine-tuning and evaluation code is on GitHub under GPL-2.0, alongside the list of open datasets used at each stage. Source: https://arxiv.org/abs/2411.05046 Read the article: https://llmobile.news/ticker/phonelm/ResearchOpen weightsAndroidNPUAgentshttps://llmobile.news/ticker/smollm2/https://llmobile.news/ticker/smollm2/Thu, 31 Oct 2024 19:30:00 +0100Hugging Face released SmolLM2 on October 31, 2024, a second generation of its small language models at 135M, 360M and 1.7B parameters. The company trained the 1.7B model on 11 trillion tokens, against 1T for the first SmolLM at the same size, and its model cards put the 360M model at 4T tokens and the 135M at 2T. The mixture combines FineWeb-Edu, DCLM and The Stack with datasets Hugging Face curated for this run, which its paper from February 2025 names as FineMath for mathematics, Stack-Edu for code and SmolTalk for instruction tuning. That paper states the 1.7B run used a multi-stage schedule that held the highest quality maths and code data back for the final tenth of training, from 10T to 11T tokens, while the 360M and 135M models trained in a single stage on a mix kept consistent throughout. Hugging Face fine-tuned the instruct versions on SmolTalk and then tuned them further on ranked answer pairs from UltraFeedback. Hugging Face reports the 1.7B base model at 60.5 on ARC against 49.2 for Llama 3.2 1B and 58.5 for Qwen2.5 1.5B, 36.7 on TriviaQA against 28.1 and 20.9, and 19.4 on MMLU-Pro against 11.7 and 13.7, all run zero-shot with its own lighteval harness and printed in the model card. The instruct version scores 56.7 on the IFEval instruction-following test against 53.5 for Llama 3.2 1B Instruct and 47.4 for Qwen2.5 1.5B Instruct, while Qwen2.5 stays ahead on the MT-Bench chat test at 6.52 to 6.13. At the smaller sizes Hugging Face compares the 360M model with Qwen2.5 0.5B, reporting 53.0 against 45.4 on ARC and 16.9 against 4.3 on TriviaQA, and publishes no third-party comparison for the 135M model. Table: Hugging Face's own figures for the base models. The weight files run 269 MB for the 135M model, 724 MB for the 360M and 3.42 GB for the 1.7B in bfloat16, and Hugging Face uploaded 4-bit ONNX builds the same day at 118 MB, 273 MB and 1.11 GB, plus a 4-bit GGUF of the 1.7B instruct model at 1.06 GB. The repository says the models run locally through llama.cpp, MLX, MLC and transformers.js. All six checkpoints carry an 8k context window and the Apache 2.0 license. Update, November 27, 2024. Hugging Face published a browser chat demo that runs the 1.7B instruct model through transformers.js and ONNX Runtime Web on WebGPU, with no server call after the download. Its local inference guide adds runnable code for llama.cpp, MLX and MLC, and lists the models on Ollama and in the PocketPal-AI phone app. Source: https://github.com/huggingface/smollm Read the article: https://llmobile.news/ticker/smollm2/Open weightsBenchmarksOpen sourcellama.cppWebGPUhttps://llmobile.news/ticker/small-model-training-bottlenecks/https://llmobile.news/ticker/small-model-training-bottlenecks/Fri, 25 Oct 2024 12:30:00 +0200Seven Apple researchers measured what it costs to train language models up to 2B parameters on rented Nvidia GPUs and found the most expensive card was the worst buy. The H100-80GB returned the fewest tokens per dollar of the three cards tested at every one of their four model sizes, 100M, 500M, 1B and 2B, while costing 2.53 times as much per hour as the A100-40GB. The price scale comes from normalising published hourly rates at Lambda Labs and Google Cloud, with the A100-40GB set at 1.00 and the A100-80GB landing at 1.46. The same comparison shows cost efficiency falling as GPUs are added, with a single card the best or near-best point at all four model sizes. For the 1B model, tokens per dollar drop from about 17,000 on one GPU to about 10,300 on 64. FlashAttention mattered more at small scale than at large. Reading the authors’ first figure, the 100M model gained roughly three times the tokens per dollar over vanilla attention, while at 2B the gain was under two times. The authors put this down to the cost of attention being quadratic in context length, so it dominates once the hidden dimension shrinks and the small models become bound by data movement between CPU and GPU and between GPUs. Vanilla attention also ran out of memory at a global batch size of 1024 on the 1B and 2B models, where FlashAttention did not. Two habits carried over from large-model training did not pay off. Plain Distributed Data Parallel, which keeps a full copy of the model on every GPU, beat both sharded FSDP variants for the smaller models because it moves less data, and FSDP only pulled ahead at 2B, where it kept training at a global batch size that made DDP run out of memory. Filling the cards was not cost-optimal either, since the batch size figure peaks around a global batch size of 128 for the 100M model, 32 for 500M, 16 for 1B and 8 for 2B, in every case well short of what the memory allowed. These are training runs on datacentre GPUs, not anything running on a phone, and the authors frame the work as guidance for labs with small budgets rather than for deployment. They name their own limits, running each setup at least three times for only 10 training steps, reporting normalised price ratios rather than absolute dollars, using no gradient accumulation, and leaving GPU utilisation and memory bandwidth measurements to future work. They also assume that settings such as the learning rate can be retuned to converge at whichever hardware configuration turns out cheapest. The 8-page paper is free to read on arXiv under the site’s non-exclusive distribution licence. Source: https://arxiv.org/abs/2410.19456 Read the article: https://llmobile.news/ticker/small-model-training-bottlenecks/AppleNvidiaResearchBenchmarksPyTorchhttps://llmobile.news/ticker/ministral/https://llmobile.news/ticker/ministral/Wed, 16 Oct 2024 10:00:00 +0200Mistral AI introduced Ministral 3B and Ministral 8B on October 16, 2024, two models the company built for on-device computing and at-the-edge use and announced together as les Ministraux. Mistral says Ministral 3B already outperforms its own Mistral 7B on most benchmarks, and reports the base model scoring 60.9 on MMLU against 52.4 for Gemma 2 2B and 56.2 for Llama 3.2 3B. Both models handle up to 128k tokens of context, which the announcement says was capped at 32k when run on vLLM. Ministral 8B uses what Mistral calls an interleaved sliding-window attention pattern, where a layer attends only to a limited span of recent tokens instead of the whole sequence, which the company says makes inference faster and lighter on memory. Mistral reports the instruction-tuned Ministral 3B at 8.1 on MTBench, 64.3 on Arena Hard and 77.4 on HumanEval, the last of those above Gemma 2 2B at 42.7 and Llama 3.2 3B at 61.0. Ministral 8B reaches 8.3 on MTBench and 70.9 on Arena Hard in the same table, while Gemma 2 9B stays ahead on WildBench at 43.8 against 41.3. All of these are Mistral’s own numbers, produced with an internal framework the company says it used to re-evaluate every model for the comparison. Table: Mistral AI's own figures, from its internal evaluation framework. Mistral names on-device translation, smart assistants that work without an internet connection, local analytics and autonomous robotics as the applications customers and partners asked for, citing demand for local, privacy-first inference. The company also positions the two models as intermediaries in front of larger ones such as Mistral Large, tuned to parse input, route tasks and call APIs across multiple contexts in multi-step agent workflows. Both models went live through Mistral’s API on the day of the announcement, at $0.04 per million tokens for Ministral 3B and $0.1 per million for Ministral 8B, with input and output priced the same. Mistral published weights for Ministral 8B Instruct only, for research use, and lists that model under both the Mistral Research License and the Mistral Commercial License, while Ministral 3B appears under the commercial license alone with no weights release. Anyone wanting to self-deploy either model has to contact Mistral for a commercial license, and the company says it will help with lossless quantisation for specific use cases. Source: https://mistral.ai/news/ministraux/ Read the article: https://llmobile.news/ticker/ministral/MistralBenchmarksTranslationAgentshttps://llmobile.news/ticker/amd-llama-135m/https://llmobile.news/ticker/amd-llama-135m/Fri, 27 Sep 2024 19:00:00 +0200AMD released AMD-Llama-135M on September 27, 2024, its first small language model, at 135M parameters and trained from scratch on AMD’s own accelerators. AMD states that pretraining used 670B tokens drawn from SlimPajama and Project Gutenberg and took six days on four Instinct MI250 nodes holding four MI250 accelerators each. A second variant, AMD-Llama-135M-code, adds 20B tokens from the Python subset of StarCoder over another four days on the same hardware. That code variant exists to work as a draft model for speculative decoding, where a small model guesses several tokens in one pass and a larger model then checks them, so the larger model can accept several tokens per forward pass rather than producing one at a time. AMD paired it with Meta’s CodeLlama-7b and measured throughput on the HumanEval coding benchmark. AMD reports about 2.8x higher throughput on an Instinct MI250 accelerator, about 3.88x on the CPU of a Ryzen AI processor, and about 2.98x with the 7B model running 4-bit on the Ryzen AI NPU while the draft model ran on the CPU, each against the same setup without speculative decoding. AMD’s footnotes name a Ryzen 9 PRO 7940HS as the test machine for the Ryzen AI numbers. Diagram: AMD, showing its own measured speedups. AMD published the weights on Hugging Face and the training, dataset and inference code on GitHub, under the Apache 2.0 license. AMD states the training code builds on TinyLlama and uses PyTorch FSDP for distributed training across nodes. Source: https://www.amd.com/en/developer/resources/technical-articles/introducing-amd-first-slm-135m-model-fuels-ai-advancements.html Read the article: https://llmobile.news/ticker/amd-llama-135m/AMDLlamaNPUOpen weightsOpen sourcehttps://llmobile.news/ticker/llama-3-2-1b-3b/https://llmobile.news/ticker/llama-3-2-1b-3b/Wed, 25 Sep 2024 19:00:00 +0200Meta released Llama 3.2 on September 25, 2024, including text-only 1B and 3B models built for phones and edge hardware. Both carry a 128K token context window and are aimed at summarisation, instruction following and rewriting that run locally, with the data staying on the device. Meta built them by structured pruning from Llama 3.1 8B, then recovered quality through knowledge distillation using logits from the 8B and 70B models during pretraining. The company reports the 3B model ahead of Gemma 2 2.6B and Phi 3.5-mini on instruction following, summarisation, prompt rewriting and tool use, and puts the 1B model on a par with Gemma. In Meta’s own table the 3B model scores 77.4 on IFEval against 61.9 for Gemma 2 2B and 59.2 for Phi-3.5-mini, and 67.0 on BFCL V2 for tool use against 27.4 and 58.4. Table: Meta. The company measured the Gemma and Phi results itself. The models shipped with day-one support for Qualcomm and MediaTek silicon and run on Arm, which Meta says covers 99 percent of mobile devices. Weights are on llama.com and Hugging Face, with deployment paths through PyTorch ExecuTorch for devices and Ollama for single-node setups, and the company lists more than 25 partner platforms at launch. Source: https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/ Read the article: https://llmobile.news/ticker/llama-3-2-1b-3b/MetaLlamaOpen weightsQualcommMediaTekhttps://llmobile.news/ticker/olmoe/https://llmobile.news/ticker/olmoe/Tue, 03 Sep 2024 19:08:20 +0200Ai2 published OLMoE-1B-7B on September 3, 2024, a language model with 6.9B total parameters of which only 1.3B run on any given token. It is a sparse mixture of experts, meaning each layer holds 64 small feedforward networks and a small router picks 8 of them per token, so the rest of the model stays idle. Compute per token lands near that of a dense 1B model, while all 6.9B parameters still have to be held in memory. Ai2 pretrained the model on 5 trillion tokens and reports in the paper that it leads every open model in its active-parameter class. On MMLU the paper puts OLMoE-1B-7B at 54.1, against 48.5 for DCLM-1B, 33.6 for TinyLlama-1B and 32.1 for Ai2’s own dense OLMo-1B. The paper states the model matches or outperforms the dense OLMo-7B at the end of pretraining while using less than half the training FLOPs, and that it beats Llama2-7B on MMLU, scored at 46.2, despite needing roughly 6 to 7 times less compute per forward pass. The instruction-tuned version is reported above Llama2-13B-Chat and DeepSeekMoE-16B. For the training-cost claim the authors ran a controlled comparison, training a 1.3B dense model and a 1.3B active, 6.9B total mixture of experts on 128 H100 GPUs for 130B tokens each. Ai2 reports the sparse model reaching the dense model’s final quality with about 3 times fewer tokens and FLOPs, but only about 2 times faster in wall-clock time, because the larger weight set dropped throughput to 23,600 tokens per second per GPU against 37,500 for the dense run. The full pretraining run used 256 H100 GPUs for around 10 days. Ai2 shipped an iOS app on February 11, 2025 that keeps the whole model on the device, so prompts and responses never reach a server and generation keeps working in airplane mode. Ai2 names the iPhone 15 Pro and 15 Pro Max, the four iPhone 16 models and M-series iPads going back to the 2021 iPad Pro, and states that the first version needs an iOS device with 8 GB of memory. The app runs a 4-bit quantised build of the January 2025 instruction-tuned model, which Ai2 measures at 41 tokens per second on average on an iPhone 16 Pro, on a stack Ai2 built with GenUI on top of llama.cpp. Weights, the training data mix, the training code and the run logs are all published under Apache 2.0, together with 244 intermediate checkpoints. The comparison table in the paper lists OLMoE as the only mixture-of-experts release that opens all four, with most of the other models in the table offering a single checkpoint and no logs. The app is in the App Store and its source code is on GitHub. Screenshot: Ai2. Update, April 14, 2025. Ai2 kept working on the app after the February launch and added macOS support on March 7, 2025 through Mac Catalyst, Apple’s compatibility layer that runs the same iOS code natively on a Mac, together with a counter that shows how many tokens a reply used. A later version reworked the panel that reports generation metrics, and the most recent release, from April 14, 2025, renamed the app to Ai2 OLMoE. Nothing has shipped in the repository since. Source: https://arxiv.org/abs/2409.02060 Read the article: https://llmobile.news/ticker/olmoe/Ai2Mixture of expertsiOSOpen weightsResearchhttps://llmobile.news/ticker/gemma-2/https://llmobile.news/ticker/gemma-2/Wed, 31 Jul 2024 17:00:00 +0200Google released Gemma 2 2B on July 31, 2024, an open model of 2.6B parameters that Google says produces outsized results by learning from larger models through distillation. Google reports it at 1126 Elo on the LMSYS Chatbot Arena leaderboard, captured on July 30, 2024, ahead of Mixtral 8x7B Instruct at 1114, GPT 3.5 Turbo 0314 at 1106 and Llama 2 70B chat at 1093, and states that the model surpasses all GPT-3.5 models there. Chart: Google, from the announcement. Scores are Google's own reading of the leaderboard on July 30, 2024. The technical report describes training the 2B size on 2 trillion tokens with knowledge distillation, where a bigger teacher model supplies a probability for every token in context and the small student is trained to match those probabilities rather than to predict the next token. In an ablation the authors ran, a 2B model trained over 500B tokens distilled from a 7B teacher averaged 67.7 across three benchmarks, against 60.3 for the same model trained from scratch. The released model holds 2.02B non-embedding parameters plus 590M embedding parameters, which come from the 256k-entry Gemini vocabulary, and it interleaves local sliding-window attention with global attention over an 8192-token context. Google DeepMind trained it on 512 TPUv5e chips. Google names edge devices and laptops as deployment targets next to cloud serving on Vertex AI and Google Kubernetes Engine, and says the model is small enough for the free tier of T4 GPUs in Google Colab. For running it locally Google points to Ollama and to Gemma.cpp, its own lightweight C++ inference engine, and says support in MediaPipe, the on-device inference stack for Android, iOS and the web, is coming. Google adds that NVIDIA optimised the model with the TensorRT-LLM library for GeForce RTX cards and for Jetson modules at the edge. Google put the weights on Kaggle, Hugging Face and Vertex AI Model Garden under the Gemma terms, which it calls commercially friendly for research and commercial applications, and made the model available to try in Google AI Studio. The same announcement added ShieldGemma, a set of safety classifiers built on Gemma 2 that filter hate speech, harassment, sexually explicit and dangerous content, and Gemma Scope, over 400 sparse autoencoders for inspecting the inner workings of Gemma 2 2B and 9B. Source: https://developers.googleblog.com/en/smaller-safer-more-transparent-advancing-responsible-ai-with-gemma/ Read the article: https://llmobile.news/ticker/gemma-2/GoogleGemmaDistillationOpen weightsBenchmarkshttps://llmobile.news/ticker/spectra/https://llmobile.news/ticker/spectra/Wed, 17 Jul 2024 07:53:00 +0200Nolano AI, the University of Montreal and Mila published Spectra on July 17, 2024, a suite of 54 language models covering nine parameter counts from 99M to 3.9B, each trained on the same 300B tokens of SlimPajama. Every size exists as a half-precision model the authors call FloatLM, as GPTQ-quantised copies at 3, 4, 6 and 8 bits called QuantLMs, and as a TriLM, a model trained from scratch with every linear-layer weight restricted to -1, 0 or 1. The paper’s size table puts TriLM 3.9B at 10.76 billion bits, roughly 1.3 GB, against 63.83 billion bits for the half-precision model of the same parameter count, a factor of 5.9. That is the comparison the suite is built for, since what fits on a device is bits rather than parameters. Averaged over six commonsense and reasoning benchmarks, the paper’s tables put TriLM 3.9B at 60.7 against 61.4 for FloatLM 3.9B, level with the 4-bit QuantLM 3.9B at 60.7 which needs 20.59 billion bits. Set against the models nearest to it in size, TriLM 3.9B beats the half-precision 830M model, 60.7 against 53.3 at 13.34 billion bits, and the 3-bit QuantLM 2.4B, 60.7 against 54.7 at 10.95 billion bits. On LAMBADA it reaches 61.6 against 61.1 for FloatLM 3.9B, and on TriviaQA 21.3 against 21.5. Chart: Kaushal et al. Averages across Arc Easy, Arc Challenge, BoolQ, HellaSwag, PIQA and WinoGrande. During training a TriLM keeps floating-point latent weights and ternarises them on the fly in the forward pass, with one scale value per weight matrix shard and gradients passed through by a straight-through estimator, so only the ternary states and the scales are needed at inference. Embeddings and the output head stay in half precision across the suite. The authors position TriLM against BitNet b1.58, which also trains ternary weights from scratch, and say they normalise once before each linear layer instead of normalising, scaling and quantising activations to 8 bits four to seven times per transformer block; their own replication of the BitNet architecture at 1.1B, trained on 100B tokens, scores below TriLM 1.1B on the same benchmarks. The paper names the weaknesses it measured. At 1.1B parameters the ternary model does not reach its half-precision counterpart, 51.6 against 54.9 on the six-benchmark average, and the 2.4B one still trails at 57.3 against 59.2, so the match with a half-precision model of the same parameter count holds at 3.9B only. On CrowsPairs, which measures how often a model prefers a stereotyped sentence, TriLM 3.9B lands at 65.4 percent against 64.7 for FloatLM 3.9B and 62.6 for the smaller FloatLM 830M, so the ternary model stereotypes more than the half-precision model closest to it in bits. No model in the suite beats random guessing on TruthfulQA, and while TriLM 3.9B holds up on Penn Treebank and LAMBADA perplexity, it stays behind FloatLM 3.9B on web corpora including Dolma, C4 and RefinedWeb, which the authors read as weaker memorisation of training data. The weights and more than 500 intermediate checkpoints are on Hugging Face under Apache 2.0. The TriLM checkpoints are published unpacked to FP16, which the model cards say makes them compatible with ordinary half-precision matrix multiplication and leaves the released files at full half-precision size. The authors list smartphones, laptops and automobiles among the resource-constrained devices they see for ternary models, and end by calling for work on the toxicity, stereotyping and web-corpus gaps they measured. Source: https://arxiv.org/abs/2407.12327 Read the article: https://llmobile.news/ticker/spectra/ResearchQuantisationMemoryOpen weightsBenchmarkshttps://llmobile.news/ticker/smollm/https://llmobile.news/ticker/smollm/Tue, 16 Jul 2024 16:00:00 +0200Hugging Face released SmolLM on July 16, 2024, a family of three base models at 135M, 360M and 1.7B parameters. The team says the models are designed to be small enough to run locally across a range of hardware, from smartphones to laptops, and cites the iPhone 15 with 6 GB of DRAM and the iPhone 15 Pro with 8 GB as reference points. Hugging Face trained the 135M and 360M models on 600B tokens each and the 1.7B model on 1T tokens. The substance is the training data, released as SmolLM-Corpus. Cosmopedia v2 contributes 28B tokens of synthetic textbooks, stories and articles generated by Mixtral-8x7B-Instruct, 39 million documents built from a predefined list of 34,000 topics taken from the BISAC book classification instead of from unsupervised clustering. FineWeb-Edu contributes 220B deduplicated tokens of educational web pages, pulled out of FineWeb by a quality classifier trained on annotations from Llama3-70B-Instruct. Python-Edu applies the same idea to code, keeping 4B tokens of the 40B Python tokens in The Stack that scored 4 or higher on an educational code classifier. On Hugging Face’s own evaluation, run with the same lighteval setup for every model it could download, SmolLM-135M averages 39.50 across MMLU, TriviaQA, ARC, PIQA, HellaSwag, OpenBookQA and Winogrande, against 32.75 for GPT2-137M and 32.49 for Pythia-160M. SmolLM-360M averages 44.44 against 39.60 for Qwen2-500M and 39.32 for Qwen1.5-500M, and SmolLM-1.7B averages 51.97 against 50.36 for Microsoft’s Phi-1.5 and 47.98 for Qwen2-1.5B. Meta’s MobileLLM is not publicly available, so Hugging Face took its per-benchmark numbers from the paper and reports no average for those rows. Table: Hugging Face's own figures, evaluated with the same setup for every model except MobileLLM. Hugging Face also benchmarked the memory footprint of each size. SmolLM-135M takes 269.03 MB in bf16, 162.87 MB at int8 and 109.78 MB at int4. SmolLM-360M takes 723.65 MB, 409.07 MB and 251.79 MB across the same three precisions, and SmolLM-1.7B takes 3422.76 MB, 1812.14 MB and 1006.84 MB. Table: Hugging Face. The weights are on Hugging Face under Apache-2.0, with ONNX checkpoints published beside the transformers ones and a GGUF build for llama.cpp announced as planned. Hugging Face also put up browser demos of the instruction-tuned 135M and 360M models that run client-side through WebGPU, with no server call. The corpus is released separately under ODC-By. Source: https://huggingface.co/blog/smollm Read the article: https://llmobile.news/ticker/smollm/Hugging FaceOpen weightsBenchmarksMemoryWebGPUhttps://llmobile.news/ticker/qwen2/https://llmobile.news/ticker/qwen2/Mon, 15 Jul 2024 14:35:00 +0200Alibaba’s Qwen Team published the Qwen2 Technical Report on July 15, 2024, covering five models from 0.5B to 72B parameters. The report states that the two smallest, Qwen2-0.5B and Qwen2-1.5B, are designed for easy deployment on portable devices such as smartphones, earphones and smart glasses, while the larger sizes target GPUs. Qwen2-1.5B scores 56.5 on MMLU, a multiple-choice knowledge test, against 52.7 for Microsoft’s Phi-2, which the same table credits with about twice the non-embedding parameters. Both small sizes tie the input embedding to the output layer, so a single weight matrix does both jobs and the shared vocabulary of 151,646 tokens is stored once rather than twice. The report puts non-embedding parameters at 0.3B for Qwen2-0.5B and 1.2B for Qwen2-1.5B. All five models use grouped query attention, where several query heads share one set of key and value heads, with 14 query heads over 2 key-value heads at 0.5B and 12 over 2 at 1.5B, which the authors say lowers the key-value cache size per token against Qwen1.5 and with it the memory footprint during long-context inference. Alibaba trained Qwen2-0.5B on a 12 trillion token dataset and the other dense models on a 7 trillion token one, reporting that the larger corpus did not pay off at the bigger sizes. For the instruction-tuned versions the report gives Qwen2-1.5B-Instruct 52.4 on MMLU, 61.6 on the GSM8K grade-school maths set and 29.0 on IFEval, which checks whether a model obeys explicit formatting instructions, against 37.9, 40.1 and 20.0 for Qwen2-0.5B-Instruct. The authors attribute the gain over the similarly sized Qwen1.5 models mainly to that data scaling. Context length differs across the family. The authors tested retrieval from long documents with Needle in a Haystack and report that the two small models handle 32K tokens, Qwen2-57B-A14B-Instruct 64K, and the 7B and 72B instruction-tuned models 128K with the YARN extension applied. The pre-training data covers approximately 30 languages, among them English, Chinese, Spanish, French, German, Arabic, Russian, Korean, Japanese, Thai and Vietnamese. Alibaba put the weights on Hugging Face and ModelScope. The Qwen2 blog post of June 7, 2024 states that Qwen2-0.5B, 1.5B, 7B and 57B-A14B carry the Apache 2.0 license while Qwen2-72B and its instruction-tuned variant stay on Alibaba’s own Qianwen License, and it names MLX, llama.cpp, Ollama and LM Studio among the projects that run the models locally. Source: https://arxiv.org/abs/2407.10671 Read the article: https://llmobile.news/ticker/qwen2/AlibabaQwenOpen weightsResearchWearableshttps://llmobile.news/ticker/mobileclip/https://llmobile.news/ticker/mobileclip/Fri, 21 Jun 2024 02:15:00 +0200Five Apple researchers presented MobileCLIP at CVPR 2024 on June 20, a family of image-text models built for phones. They exported each model with Core ML Tools 7.0 and timed it at batch size 1 on an iPhone 12 Pro Max running iOS 17.0.3, and their Table 7 gives the smallest variant, MobileCLIP-S0, 1.5 ms for the image encoder and 1.6 ms for the text encoder at 67.8% zero-shot accuracy on ImageNet. The same authors measure OpenAI’s ViT-B/16 CLIP on that phone at 11.5 ms and 3.3 ms for 68.3%, and both models average 58.1 across the 38 datasets of the DataComp evaluation suite. Multi-modal reinforced training, the method the paper introduces, moves the expensive part of distillation out of the training loop and into the dataset. Apple reinforces the DataComp dataset once, writing five synthetic captions per image with the CoCa model in OpenCLIP, storing the parameters of strong random image augmentations, 30 per image for the 12M subset and 10 for the billion-sample set, and storing the embeddings that two ViT-L/14 teacher models produce for those augmented images and for the real and synthetic captions. Training afterwards reads the captions and the teacher embeddings from disk, so the captioning model and the teachers never run again however many models are trained on the set. The paper puts numbers on what that saves. One epoch over the 12.8M samples of the reinforced set on a single node of 8 A100-80GB GPUs takes 1.3 hours, the same as an epoch on the unreinforced data, against 21.1 hours when the captioning model and the teacher ensemble run during training, according to Table 4d. The cost moves to storage, and Table 4c puts DataComp-1B at 90 TB against 140 TB for the reinforced DataCompDR-1B, with the 12M subset going from 0.9 TB to 1.9 TB. MobileCLIP comes in four sizes, and Table 7 lists image and text encoder parameters separately. S0 pairs an 11.4M-parameter image encoder with a 42.4M-parameter text encoder, S1 uses 21.5M and 63.4M at 2.5 ms and 3.3 ms, S2 uses 35.7M and 63.4M at 3.6 ms and 3.3 ms, and MobileCLIP-B keeps a standard ViT-B/16 image encoder at 86.3M and 10.4 ms. Zero-shot ImageNet accuracy across the four runs 67.8%, 72.6%, 74.4% and 76.8%, and a version of B trained on 39B seen samples instead of 13B reaches 77.2%. All three small variants replace the vision transformer with a convolution-transformer hybrid derived from Apple’s FastViT, and S0 also swaps the text encoder for one that mixes 1-D convolutions with four self-attention layers, which the authors report as 5% smaller and 15.8% faster than the 12-layer transformer text encoder that S1 and S2 keep. Apple released the code, the weights and the reinforced data at apple/ml-mobileclip, with checkpoints for all four variants and the longer-trained B, and with DataCompDR-1B and DataCompDR-12M on Hugging Face as synthetic captions, augmentation parameters and teacher embeddings keyed to DataComp images rather than as the images themselves. The repository lists the code under MIT, the models under Apple’s own machine learning research model terms, and the data under CC BY-NC-ND. Apple showed zero-shot scene classification running in real time on an iPhone in the CVPR exhibit hall, and added an iOS app for the same task to the repository in November 2024. Source: https://openaccess.thecvf.com/content/CVPR2024/html/Vasu_MobileCLIP_Fast_Image-Text_Models_through_Multi-Modal_Reinforced_Training_CVPR_2024_paper.html Read the article: https://llmobile.news/ticker/mobileclip/AppleiPhoneResearchBenchmarksOpen weightshttps://llmobile.news/ticker/fox/https://llmobile.news/ticker/fox/Thu, 13 Jun 2024 15:00:00 +0200TensorOpera released Fox-1 on June 13, 2024, a decoder-only language model with 1.6B parameters trained on 3 trillion tokens of text and code. The company says the run followed a three-stage data curriculum at 8K sequence length, without saying what each of the three stages contained. Fox-1 uses grouped query attention, where several attention heads share one set of key and value vectors to cut memory traffic, with 16 attention heads over 4 key-value heads. TensorOpera describes the model as deeper than others of its size, 78% deeper than Gemma-2B, 33% deeper than Qwen1.5-1.8B and StableLM-2-1.6B, and 15% deeper than Apple’s OpenELM-1.1B. TensorOpera reports an average of 47.13% across ARC Challenge, HellaSwag, MMLU, TruthfulQA, Winogrande and GSM8k, which it ran in the Open LLM Leaderboard setup on one machine with 8 H100 GPUs. It puts Qwen1.5-1.8B at 46.81%, Gemma-2B at 46.36%, StableLM-2-1.6B at 45.92% and OpenELM-1.1B at 38.28%. Fox-1 leads that group on GSM8k with 36.39% against 34.04% for Qwen1.5-1.8B and 17.06% for Gemma-2B, and trails Qwen1.5-1.8B on MMLU with 43.05% against 47.15%. Chart: TensorOpera's own figures. The device claim in the announcement is a target rather than a measurement. TensorOpera puts the model’s FP16 memory use at 3,703 MiB, against 5,379 MiB for Gemma-2B, 4,739 MiB for Qwen1.5-1.8B and 3,852 MiB for StableLM-2-1.6B, and says that makes Fox-1 comparable for on-device deployment. The stated plan is that developers train on the company’s cloud platform and then deploy, personalise and monitor models on smartphones and AI-enabled PCs through its FedML platform. No figure for speed or latency on a phone appears in the post, and the throughput number it does give, over 200 output tokens per second per user, was measured with vLLM on a single H100. Chart: TensorOpera's own figures. TensorOpera published the base model under the Apache 2.0 license on Hugging Face and on its own platform, and said an instruction-tuned version would follow. Source: https://blog.tensoropera.ai/tensoropera-unveils-fox-foundation-model-a-pioneering-open-source-slm-leading-the-way-against-tech-giants/ Read the article: https://llmobile.news/ticker/fox/TensorOperaOpen weightsBenchmarksMemoryhttps://llmobile.news/ticker/apple-intelligence-foundation-models/https://llmobile.news/ticker/apple-intelligence-foundation-models/Mon, 10 Jun 2024 21:00:00 +0200Apple introduced Apple Intelligence on June 10, 2024, built on a roughly 3-billion-parameter language model that runs on the device and a larger model that runs on Apple silicon servers through Private Cloud Compute. The on-device model uses a 49K token vocabulary, the server model 100K. Apple compresses the on-device model with a mix of 2-bit and 4-bit weights, averaging 3.7 bits per weight, and says an option at 3.5 bits per weight costs no significant quality. On an iPhone 15 Pro the company reports a time to first token of about 0.6 milliseconds per prompt token and 30 tokens per second, both measured before token speculation. Rather than ship a separate model per feature, Apple swaps LoRA adapters on top of the shared base model. The adapters are stored in 16 bits and, at rank 16, take tens of megabytes each. Apple says human graders preferred its models over comparable ones including GPT-3.5, GPT-4, Llama, Mistral and Gemma across its evaluations. Diagram: Apple. Source: https://machinelearning.apple.com/research/introducing-apple-foundation-models Read the article: https://llmobile.news/ticker/apple-intelligence-foundation-models/AppleiPhoneiOSQuantisationApple Siliconhttps://llmobile.news/ticker/powerinfer-2/https://llmobile.news/ticker/powerinfer-2/Mon, 10 Jun 2024 16:01:21 +0200Researchers at the IPADS lab of Shanghai Jiao Tong University published PowerInfer-2 on June 10, 2024, an inference framework for running language models that exceed a phone’s memory. On a OnePlus 12, a Snapdragon 8 Gen 3 device with 24 GB of DRAM of which 19 GB can be occupied by an app, the authors measure TurboSparse-Mixtral-47B decoding at 11.68 tokens per second, and describe it as the first system to serve a 47B model on a smartphone. In the same configuration they report 3.12 times the speed of LLMFlash and 21.2 times the speed of llama.cpp. LLMFlash here is their own reimplementation of Apple’s LLM in a flash techniques inside llama.cpp, which they built because that work was never released as code. PowerInfer-2 splits each feedforward layer into neuron clusters, small groups of neurons that tend to activate together, and treats a cluster rather than a whole matrix as the unit of work. Frequently activated clusters go to the phone’s NPU for dense arithmetic, while rarely activated ones go to the CPU, which copes better with irregular sparse work. Weights that are not already in an in-memory cache are read from UFS flash on demand, and those reads are overlapped with computation so that a core can work on one cluster while the next is still arriving. For prompt processing the system loads whole layers with sequential reads, which the authors measure at 3 times the throughput of random reads, and prefetches the next layer while the current one computes. The authors vary how much memory the app may use from 7 GB to 19 GB with the 47B model. At 7 GB the cache holds only 1.8 percent of the feedforward weights, so nearly every neuron has to be fetched from flash for every token and decoding falls to 2.13 tokens per second, which is still 1.84 times LLMFlash. The 47B model activates only about 3B parameters per token thanks to its mixture-of-experts structure and added sparsity, which is why it decodes at roughly the pace of a 7B model in the same test. Streaming weights off storage to fit a large model into a small budget is the same bet Edge0 makes at the level of whole experts rather than individual neurons. Chart: Xue et al. LLMFlash is the authors' own reimplementation of Apple's method. On OpenCompass at 4-bit weights, the authors put PowerInfer-2 at an average of 78.38 across Arc-Challenge, Arc-Easy, MMLU and GSM8K for Qwen2 7B against 79.25 for llama.cpp, and at 68.35 against 70.12 for Bamboo-7B, with Qualcomm’s QNN engine at 56.93 and 63.26. Token latency is uneven, and for the 47B model with half the feedforward weights offloaded they report a mean of 99.76 ms per token and a 99th percentile of 140.56 ms, tracking a cache miss rate that averages 3.5 percent but reaches 18.9 percent at the 99th percentile. Prompt processing for Bamboo-7B runs at 404.6 tokens per second with half the weights offloaded, against over 700 tokens per second with everything in memory. They measure energy at 0.257 joules per token against 0.373 for QNN and 0.672 for llama.cpp. PowerInfer-2 uses privileged system calls such as memory locking, so the authors built and ran it on rooted Android phones, and they state that nothing in the design requires kernel changes and that it should port to iOS. It also needs models with predictable sparsity, which is why the group trained sparsified TurboSparse versions of Mistral and Mixtral, and on an unmodified SiLU-based Mistral-7B the gain over LLMFlash drops to 2.4 times against 4.6 times for the ReLU-based Bamboo-7B. The system is 12,000 lines of C++ added to the group’s PowerInfer engine for PCs, which is MIT-licensed, but the announcement there links the paper and the model weights rather than a release of the PowerInfer-2 code. The paper was last revised in December 2024. Source: https://arxiv.org/abs/2406.06282 Read the article: https://llmobile.news/ticker/powerinfer-2/MemoryNPUAndroidQualcommResearchhttps://llmobile.news/ticker/openelm/https://llmobile.news/ticker/openelm/Tue, 23 Apr 2024 01:12:00 +0200Apple posted OpenELM to arXiv on April 22, 2024, a family of four language models at 270M, 450M, 1.1B and 3B parameters, each also released in an instruction-tuned version. The name stands for Open-source Efficient Language Models, and the efficiency comes from layer-wise scaling, which spreads the parameter budget unevenly across the transformer’s layers instead of giving every layer the same shape. Table 1 of the paper gives the 1.1B model an average of 45.93% across the OpenLLM leaderboard tasks against 43.57% for OLMo 1.2B, a gap the authors state as 2.36%, reached on 1.5T pre-training tokens against OLMo’s 3.0T. Layer-wise scaling varies the number of attention heads and the width of the feed-forward network from layer to layer, keeping layers near the input narrower and widening them towards the output, so a layer late in the stack holds more parameters than an early one. Apple trained the four variants for 350k iterations with its CoreNet library on public data only, drawing on RefinedWeb, a deduplicated PILE, a subset of RedPajama and a subset of Dolma v1.6, about 1.8T tokens in total. Text is filtered and tokenised on the fly rather than pre-tokenised, using the same tokeniser as Llama. The paper benchmarks the models on an Apple MacBook Pro with an M2 Max chip and 64 GiB of RAM running macOS 14.4.1, through a port of OpenELM to MLX v0.10.0, and separately on a Linux workstation with an Intel i9-13900KF and an NVIDIA RTX 4090. On the MacBook in BFloat16, Apple measures the 270M model generating 212.40 tok/s and the 3B model 33.96 tok/s, with the 4-bit versions at 256.35 and 60.33 tok/s. On the CUDA machine OpenELM 1.08B is slower than OLMo 1.18B, at 92.15 tok/s against 203.40, which the authors attribute to a naive RMSNorm implementation and to OpenELM having 113 normalisation layers where OLMo has 33. Apple presents the release as the complete framework rather than weights and inference code, covering the training and evaluation code for public datasets, the training logs, multiple checkpoints, the pre-training configurations and the code that converts a model to MLX for inference and fine-tuning on Apple devices, with pre-converted bf16 and 4-bit checkpoints for all four sizes. The eight models are on Hugging Face and the code is in CoreNet. Both the model cards and CoreNet name the terms as the Apple sample code license, which is Apple’s own license and not one of the standard open source licenses. Source: https://arxiv.org/abs/2404.14619 Read the article: https://llmobile.news/ticker/openelm/AppleApple SiliconResearchOpen weightsBenchmarkshttps://llmobile.news/ticker/phi-3-mini/https://llmobile.news/ticker/phi-3-mini/Mon, 22 Apr 2024 18:00:00 +0200Microsoft introduced Phi-3-mini on April 22, 2024, a 3.8-billion-parameter model trained on 3.3 trillion tokens. The technical report puts it at 69 percent on MMLU and 8.38 on MT-bench, which the authors say rivals models such as Mixtral 8x7B and GPT-3.5. Quantised to 4 bits the model occupies about 1.8 GB, small enough for a phone. The report describes the test directly. We tested the quantized model by deploying phi-3-mini on iPhone 14 with A16 Bionic chip running natively on-device and fully offline achieving more than 12 tokens per second. Screenshots: Microsoft, Figure 1 of the technical report. The same report covers two larger siblings, Phi-3-small at 7B parameters and Phi-3-medium at 14B, both trained on 4.8 trillion tokens. Microsoft reports 75 percent on MMLU for the small model and 78 percent for the medium one. Source: https://arxiv.org/abs/2404.14219 Read the article: https://llmobile.news/ticker/phi-3-mini/MicrosoftPhiiPhoneQuantisationOpen weightshttps://llmobile.news/ticker/octopus-v3/https://llmobile.news/ticker/octopus-v3/Wed, 17 Apr 2024 17:07:06 +0200Wei Chen and Zhiyuan Li posted Octopus v3 on April 17, 2024, a technical report on a multimodal agent model that takes an image alongside a text query and answers with a function call. The model has fewer than 1B parameters, handles English and Chinese in one set of weights, and is built to run on edge devices. The paper lists both authors at Stanford University and gives a Nexa AI address for correspondence. The model encodes each supported function as a single functional token in its vocabulary, learned the way a language model learns an unfamiliar word from the contexts it appears in, so the output is one token for the action plus its arguments and needs no parser to read. Images run through a CLIP-based encoder, which the authors say they chose after comparing encoding methods. Training runs in stages, with the language model and the image encoder trained apart, then merged and aligned, then taught the functional tokens using the method from Octopus v2, and finally tuned by reinforcement learning with another large language model acting as the reward model. The evaluation consists of 10 smartphone functions converted into functional tokens and ten worked examples, each printing the model’s output next to the output of GPT-4V feeding GPT-4, which the authors prompted with all the function descriptions and a few examples. The authors write that their model performs comparably to that pair on the selected functions. The report carries no accuracy rate, no success rate and no benchmark table, so the comparison rests on the ten printed generations rather than on a measured score. The report states that the model runs on devices as constrained as a Raspberry Pi and that it makes converting a Raspberry Pi into something like a Rabbit R1 or a Humane AI Pin practical with an on-device model instead of a cloud service. It names no Raspberry Pi board, no phone and no other hardware it was run on, and gives no latency, no memory footprint and no throughput, so the edge deployment is asserted rather than shown. The authors do name one cost themselves, writing that vision input may introduce considerable latency and that they were optimising inference speed. Weights and inference code were available only on application when the report went up, which describes the model as still under testing and for research purposes only, and the authors state that the functional token is licensed. The project page that hosted the demo videos no longer resolves. The authors list audio and video as the modalities they want to support next. Source: https://arxiv.org/abs/2404.11459 Read the article: https://llmobile.news/ticker/octopus-v3/Nexa AIAgentsResearchhttps://llmobile.news/ticker/octopus-v2/https://llmobile.news/ticker/octopus-v2/Tue, 02 Apr 2024 11:01:00 +0200Wei Chen and Zhiyuan Li published Octopus v2 on April 2, 2024, a 2B function-calling model fine-tuned from Google’s Gemma 2B that turns a spoken or typed request into an Android API call. On their own evaluation set built from 20 Android APIs, the authors report 99.524% accuracy and 0.38 seconds per call for Octopus v2, against 98.571% and 1.02 seconds for GPT-4. Both authors give Stanford University as their affiliation on the paper and publish the weights under the name Nexa AI. The distinguishing idea is what the authors call a functional token. Each of the 20 APIs gets its own special token added to the tokenizer and to the model’s output head, so picking a function becomes a single choice among 20 token candidates instead of writing the function name out as text and hoping every piece of it lands correctly. Since the model learned during fine-tuning what each of those tokens stands for, the function descriptions no longer have to sit in the prompt, which the paper puts at over 95% less context. Retrieval-augmented function calling processes roughly 1,000 tokens per call in their comparison, while Octopus v2 sees the query alone, typically under 30 tokens. Shorter context is also where the speed comes from. The authors measure Llama-7B with retrieval-augmented function calling at 68.095% accuracy and 13.46 seconds per call on a single NVIDIA A100, and Octopus v2 at 0.38 seconds under the same settings on the same A100, a 35-fold difference. The phone figure is a separate measurement. With the model quantised and the state for the fixed prompt prefix precomputed, they report a function call finishing in 1.1 to 1.7 seconds for queries of 20 to 30 tokens on what the paper calls a standard Android phone, without naming the device, and they estimate 37 times more calls per iPhone battery charge than a 7B model with retrieval. The authors name limits of their own result. Training with LoRA instead of the full model drops accuracy to 99.048%, and cutting the training set from 1,000 to 100 generated examples per API drops it to 98.095%, which they still put forward as the low end of a usable range. The set of callable functions is fixed at training time, since every API has to exist as a token before the model can emit it, and they price the data generation at 0.0224 USD per 1,000 examples per API. They also note that the GPT-3.5 and GPT-4 latencies were taken through OpenAI’s API on March 18, 2024, and may reflect API traffic or hardware allocation at that moment. Nexa AI released the weights on Hugging Face under a non-commercial CC BY-NC 4.0 license, on top of Google’s Gemma 2B. The model card said at the time that the team was still preparing the code for an open-source release. Source: https://arxiv.org/abs/2404.01744 Read the article: https://llmobile.news/ticker/octopus-v2/Nexa AIAgentsAndroidGemmaResearchhttps://llmobile.news/ticker/octopus-function-calling/https://llmobile.news/ticker/octopus-function-calling/Tue, 02 Apr 2024 03:29:28 +0200Wei Chen, Zhiyuan Li and Mingyuan Ma of Stanford University and Harvard University posted Octopus on April 2, 2024, four open models fine-tuned to pick the right software API for a request and fill in its arguments. On a benchmark the authors built themselves, they report their fine-tuned Gemma 2B answering 93 percent of calls correctly against 96 percent for GPT-4 and 50 percent for GPT-3.5, with the CodeLlama 7B and Gemma 7B versions reaching 97 percent once an output filter is switched on. The title says on-device, but nothing in the paper runs on a device. The authors fine-tuned the models on A100 80GB GPUs and queried GPT-4 and GPT-3.5 through OpenAI’s API, so every number was measured server-side. The device claim is a single sentence stating that models of this size can already be deployed on mobile, pointing at MLC LLM, and the paper names no phone, no chip and no latency, memory or throughput figure. The training set starts from roughly 30,000 of the most used APIs on RapidAPI Hub, which GPT-4 cut to about 20,000 by dropping entries with missing arguments or descriptions that did not match their parameters. The authors rewrote each API as a Python function signature with a docstring, had GPT-4 write five answerable queries per API and an equal share of unanswerable ones, and let GPT-4 verify the result, ending at about 150,000 examples as described in the paper. Vector search over the function descriptions pulled in near-identical functions as distractors, and training worked through them from easy to hard. Base models were CodeLlama 7B, Gemma 2B and 7B and Stable Code 3B, all tuned with LoRA at rank 16 and 8-bit quantisation, using 90 GPU hours for CodeLlama 7B and Gemma 7B, 60 for Stable Code 3B and 30 for Gemma 2B. The output filter the authors call a conditional mask blocks, at each generated token, anything that would break the expected format, so a function name has to come from the known list and an argument has to match its declared type. The authors state that it cannot be applied to GPT-4 or GPT-3.5 because the OpenAI API does not return the underlying token scores, and that GPT-3.5 was not counted wrong for formatting mistakes. The paper states that the training dataset and the fine-tuned models will be open sourced soon, and names no repository or date. The preprint went up under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license. Source: https://arxiv.org/abs/2404.01549 Read the article: https://llmobile.news/ticker/octopus-function-calling/Nexa AIResearchAgentsGemmaBenchmarkshttps://llmobile.news/ticker/merino-iot-language-models/https://llmobile.news/ticker/merino-iot-language-models/Wed, 28 Feb 2024 04:20:00 +0100Researchers at the University of Central Florida and the University of Technology Sydney published MeRino on February 28, 2024, a method that draws up a transformer architecture to fit a given device’s limits on parameters, arithmetic and latency, instead of shrinking an existing model to fit. Their largest design, MeRino-64M, reaches the same 0.408 average accuracy across twelve zero-shot tasks as Meta’s OPT-350M while carrying 64M parameters against 331M. The authors time it at 114 ms against 559 ms for a 128-token input on an NVIDIA Jetson Nano, or 4.9 times faster. The search scores a candidate architecture without ever training it. Each candidate shape, meaning how many blocks it has and how wide and how deep each block is, gets a single number that the authors compute from the dimensions of its attention and feedforward matrices and treat as a measure of how much information the network can represent, with a second term that penalises shapes which are deep and narrow, because those train badly. An evolutionary algorithm mutates block widths and depths for 100,000 rounds, discards everything that breaks the budget, keeps the highest-scoring survivors and returns the best one. A precomputed lookup table stands in for the matrix decomposition the score would otherwise need, and the authors put its average error at 0.03 percent. Because the score is arithmetic over an architecture’s dimensions rather than a forward pass, the search runs on the target board itself. The paper puts one MeRino search at 0.05 hours and 0.75 Wh on the Jetson Nano’s CPU, against 1.2 hours and 300 Wh for the TE-NAS architecture search on an NVIDIA GTX 1080Ti. Every device measurement in the paper comes from that Jetson Nano developer board, taken at batch size 1 and sequence length 128 and averaged over 16 runs, and the authors report no measurements on phones or tablets. Training the designed models is separate and conventional work, at 600,000 steps on 8 NVIDIA H100 GPUs. The same tables show MeRino-64M behind OPT-350M on language modelling, at 22.47 against 18.51 perplexity on WikiText-2 and 27.06 against 23.08 on Penn Treebank, where lower is better. The smallest design, MeRino-52M, matches Pythia-70M at 0.383 average zero-shot accuracy and 48 ms against 95 ms. The authors state that the weights and evaluation for Meta’s MobileLLM were not released when they submitted, so they do not compare against it, and they report that 8-bit weight quantisation leaves accuracy almost unchanged while 4-bit costs about 1 percent. The work was accepted at AAAI 2025 and the paper names no code release. Source: https://arxiv.org/abs/2403.07921 Read the article: https://llmobile.news/ticker/merino-iot-language-models/ResearchBenchmarksNvidiaMemoryhttps://llmobile.news/ticker/stable-lm-2-zephyr/https://llmobile.news/ticker/stable-lm-2-zephyr/Tue, 27 Feb 2024 20:00:00 +0100Stability AI published the Stable LM 2 1.6B technical report on February 27, 2024, describing a decoder-only model of 1.6B parameters trained from scratch on roughly 2 trillion tokens of public data. The mix is multilingual by design, with web text in German, Spanish, French, Italian, Dutch and Portuguese alongside English, and the report states the finished model handles all seven. The model has 24 layers and a context length of 4096 tokens, which the authors list as one of the things they want to extend. In the report’s multilingual table, which averages translated versions of four English benchmarks and a next-word prediction set across those seven languages, the base model scores 40.5, above Google’s Gemma 2B at 39.8 with 2.5B parameters and Microsoft’s Phi-2 at 34.6 with 2.7B. The chat variant reaches 41.5 and Stability’s own 3B model 41.7. The authors state they used no multilingual data during fine-tuning, so the language coverage comes out of pre-training alone. On the six English tasks of the Hugging Face Open LLM Leaderboard, the same report’s table puts the base model at an average of 45.3, behind Alibaba’s Qwen 1.5 1.8B at 46.6 and Gemma 2B at 46.5. The instruction-tuned StableLM 2 Zephyr 1.6B averages 49.7, the highest of the sub-2B entries in that table, ahead of Phi-1.5 at 47.7 and behind the 2.7B Phi-2 at 61.3. The report’s abstract calls Stable LM 2 1.6B the state-of-the-art open model under 2B parameters at the time of publication. Stability built the Zephyr variant in three stages. It fine-tuned the base model on seven public instruction datasets, 826,938 samples after dropping conversations longer than eight turns, then aligned the result with direct preference optimisation, which trains on pairs of preferred and rejected answers, using UltraFeedback and Intel’s Orca Pairs and borrowing most hyperparameters from the recipe behind Hugging Face’s Zephyr 7B. A third stage taught the model who made it and what a language model cannot do, using 10,000 opening messages the base model wrote for itself. The variant scores 5.42 on MT-Bench, where a larger model judges answers to open-ended questions, above Qwen 1.5 1.8B Chat at 5.29 and Gemma 2B Instruct at 5.19 and below Stability’s own StableLM Zephyr 3B at 6.64. Chart: Bellagente et al., the authors' own figures. Stability put both sets of weights on Hugging Face along with quantised builds for llama.cpp, Apple’s MLX and Intel’s OpenVINO. The report measures 127 tok/s at 11 W for the 4-bit MLX build on an M2 Mac mini with 8 GB, against 71 tok/s at 6 W in FP16, and 99 tok/s for a 4-bit GGUF build on a 2023 MacBook Pro with 16 GB, while stating these figures are meant as a practical reference rather than rigorous benchmarking. Neither release is open source. The report says the model ships under a non-commercial licence, the Zephyr model card names the Stability AI Non-Commercial Research Community License and points commercial users to the company, and the base model card now carries the Stability AI Community License with commercial use running through a Stability membership. Source: https://arxiv.org/abs/2402.17834 Read the article: https://llmobile.news/ticker/stable-lm-2-zephyr/Stability AIResearchBenchmarksOpen weightsApple Siliconhttps://llmobile.news/ticker/bitnet-b1-58/https://llmobile.news/ticker/bitnet-b1-58/Tue, 27 Feb 2024 19:56:00 +0100Microsoft Research and the University of Chinese Academy of Sciences published BitNet b1.58 on February 27, 2024, a language model in which every single weight is one of three values, -1, 0 or 1. At 3B parameters the authors measure it at 2.22 GB of memory against 7.89 GB for a full-precision Llama-style model of the same size trained on the same number of tokens, a 3.55 times reduction, with a slightly lower perplexity of 9.91 against 10.04. Three possible values need log2(3) bits to store, which is roughly 1.58 and where the name comes from. What that buys is a change to the matrix multiplication that dominates the work of running a model. A normal model multiplies each weight by an activation in floating point and sums the products, but multiplying by -1, 0 or 1 is only a sign flip, a skip or a copy, so the paper states that the matrix multiplication of BitNet “only involves integer addition”. Activations stay at 8 bits. The models are trained from scratch at this precision rather than trained in full precision and squeezed down afterwards, which is how most low-bit models on phones are produced today. The authors trained BitNet b1.58 and their own reproduced Llama baseline on the same data for the same number of tokens at each size, and compare the two directly. They report that BitNet b1.58 starts to match the full-precision baseline in perplexity at 3B and on zero-shot end tasks from 3B as well, where it averages 50.2 against 49.7. The efficiency figures widen with size. Memory drops 2.60 times at 700M, 2.93 times at 1.3B and 3.55 times at 3B, and latency per output token falls 1.23 times, 1.67 times and 2.71 times across the same three sizes. For throughput the authors ran 70B models on two A100 cards and report a batch size of 176 against 16 and 2977 tokens per second against 333, an 8.9 times gain. They also calculate that on 7nm chips the arithmetic for matrix multiplication costs 71.4 times less energy, because integer addition is far cheaper than floating-point multiply-accumulate. The paper names no model release and its arXiv listing marks it as work in progress. The authors argue the approach suits CPUs, “which are the main processors used in edge and mobile devices”, and say the lower memory and energy cost is what would let such models run there. They close by calling for new hardware and systems designed specifically for 1-bit models. Update, April 16, 2025. Microsoft released BitNet b1.58 2B4T, a 2B model trained from scratch on 4 trillion tokens at ternary precision, which the team calls the first open-source native 1-bit model at that scale. The report puts its non-embedding memory at 0.4 GB against 1.4 GB for Gemma-3 1B and 2.6 GB for Qwen2.5 1.5B, measures CPU decoding at 29 ms per token on a 13th Gen Intel Core i7-13800H using 8 threads against 41 ms and 65 ms for the same two models, and estimates energy at 0.028 J against 0.186 J and 0.347 J. Across the benchmark suite it averages 54.19, above Llama 3.2 1B at 44.90 and just under Qwen2.5 1.5B at 55.23. The weights are on Hugging Face, with a custom CUDA kernel for GPUs and CPU inference through bitnet.cpp, the runtime Microsoft published in October 2024 and measured at 2.37 to 6.17 times faster than the baseline on x86 CPUs and 1.37 to 5.07 times faster on ARM. Source: https://arxiv.org/abs/2402.17764 Read the article: https://llmobile.news/ticker/bitnet-b1-58/MicrosoftResearchQuantisationMemoryChips