Search “Hugging Face”

Mistral releases Mistral Large 4 (Le Chonk) weights on Hugging Face

Mistral released the model weights of the new version of Le Chonk (Mistral Large 4) on Hugging Face, targeting agentic coding and cybersecurity scenarios. Mistral claims that the model ranks first among open-source models on Cognition's FrontierCode benchmark and sets a new benchmark for Pareto efficiency on Artificial Analysis, while also improving inference efficiency and reducing inference costs. The model is also claimed to achieve SOTA in cybersecurity defense and has been tested by vendors such as CrowdStrike, Cloudflare, Rubrik, and Zscaler; users can customize and self-deploy it with Mistral Forge.

Mistral Large 4 weights go live on Hugging Face; the company says it leads on CyberGym-E2E and…

After a three-week preview, Mistral AI has released the weights of Mistral Large 4 on Hugging Face. This multimodal model was trained on 3,800 Nvidia accelerators and has a parameter count of one trillion, and it is currently still difficult to run on a gaming PC or a single GPU. Mistral says it leads GLM-5.3 and Kimi K3 on the cybersecurity benchmark CyberGym-E2E, and is on par with GLM-5.3 on the agentic coding benchmark Terminal Bench 4.0, but there is no independent evaluation yet; preview pricing is $1.4 per million input tokens and $4.2 per million output tokens.

MiniMax publicly releases M3.1 Flash weights on Hugging Face

MiniMax has publicly released the MiniMax M3.1 Flash weights on Hugging Face; the model has 428B total parameters and 23B activated per token. The company claims generational improvements in agent tasks, coding, and cybersecurity, with scores of 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam, and 82.2 on CyberGym. This model is the stealth model Space Bunny on OpenRouter, released on October 19, and is now available on the MiniMax platform and API.

Hugging Face, together with TIGEM and other institutions, releases the open-source DNA model Carbon-X

Thomas Wolf announced that, in collaboration with TIGEM, the University of Naples Federico II, and Zhongguancun Academy, they trained Carbon-X, an open 14B model that can directly read raw DNA and interpret it in natural language. It scores 67% on its DNA benchmark, above Claude Opus 5.5's 61% and Gemini 4 Argon's 50%, and is less than one point behind GPT-6.1 Sol while costing about 1,000 times less. The training data excludes human and animal viruses, the weights are open, and the model can run on hardware accessible to academia, such as a laptop.

Hugging Face open-sources DNA model Carbon-X, mid-trained on Qwen3-14B

Hugging Face co-founder Clément Delangue announced the open-source DNA model Carbon-X, obtained by mid-training Qwen3-14B on 300B tokens of DNA, English, and interleaved data, using a hybrid tokenizer with BPE for English and a 6-mer tokenizer for DNA. The model surpasses all open-source language models it evaluated on five DNA understanding and generation benchmarks, improving on Qwen3-14B by 27 points, with API costs 1,800 times lower than GPT 6.1 Sol. The model is released on Hugging Face under the Apache 2.0 license.

Hugging Face Releases Open-Source DNA Model Carbon-X, Runs Locally on a Laptop

Hugging Face on Thursday released the open-source DNA understanding model Carbon-X, which can run locally on a laptop, letting users ask in natural language how DNA changes affect gene activity and health. The model was developed in collaboration with researchers at Zhongguancun Academy and the University of Naples Federico II; it extends the vocabulary of an existing language model to read DNA in six-letter units and was further trained on 300 billion tokens of DNA sequences, English text, and samples pairing DNA with biological descriptions. The model has 14B parameters and can process sequences of about 50,000 base pairs at a time; it cannot upload a complete human genome. The developers also note that the prediction accuracy has not yet been validated through wet-lab experiments.

Hugging Face Releases Open-Source Model Carbon-X, Which Can Analyze and Generate DNA Sequences Using Natural Language

Hugging Face has released the open-source model Carbon-X, combining DNA analysis and generation with natural language so that researchers can ask questions directly and receive predictions and explanations. The model is based on Qwen3-14B and was trained on genomic sequences and natural language from humans, other mammals, plants, and fungi, with viruses explicitly excluded from the training data. Researchers can run it locally, avoiding the sharing of sensitive patient data with external AI companies, and can also generate new DNA sequences to assist gene therapy research.

English translation is being prepared.

Hugging Face Releases Beam: A 501B Open Source Model Supporting 1M Context and Self-Hosting

Hugging Face has released Beam, a 501B-parameter sparse MoE open source model aimed at long-text reasoning and agent tasks with 1M context, activating only about 23B parameters per token. The model uses 52 layers of hybrid attention, with 43 layers using a 512-token sliding window and 9 layers using global attention; the KV cache grows by 36 KiB per token, about one-sixth that of a full-attention approach. The official release provides three checkpoints—BF16, FP8, and NVFP4; NVFP4 can compress the weights from about 1 TB to about 280 GB, and day-0 support is already available in transformers, vLLM, and SGLang.

English translation is being prepared.

Meta open-sources 816B-parameter model Muse Spark Open on Hugging Face

Meta has released the model weights of Muse Spark Open on Hugging Face, an 816B-parameter model aimed at coding and agent tasks. The open-source version shares the same base model as Muse Spark 1.3, with additional safety post-training to reduce the risk of misuse when used standalone without system guardrails, reversing Meta's strategy of its first proprietary closed frontier model in April this year. The model has 62 layers, each containing 256 expert networks, with each token routed to 8 of them, so deployment requires the memory of an 816B-parameter model but runs at the speed of a 42B model. A 3-bit quantized version tested on a 512GB M3 Ultra occupies about 344 GB, with generation speed of about 21 tok/s under short prompts.

English translation is being prepared.