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Mistral releases Large 4 weights on Hugging Face

On October 29, 2026, Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter native multimodal model that continues reinforcement learning on the basis of the preview version, with improved coding and defensive cybersecurity capabilities. According to data published by Mistral AI, the model scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT Mistral Ecosystem Open-Weight license, and is priced at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output via API. On the same day, Unsloth released a 1-bit GGUF quantized version of the model, stating that it can run locally on a Mac Studio, retains 91% top-1 accuracy after quantization, and provides a download link and usage guide.

Pluto Security discovers 31 malicious Hugging Face repositories

Pluto Security discovered 31 malicious repositories on Hugging Face that masqueraded as small Qwen2 test checkpoints, using trust_remote_code to execute Python code before the model loaded, installing cryptocurrency mining programs and opening a reverse shell. The most-downloaded one, MENXIANGYU/qwen2-tiny, exceeded 28,000 downloads. Within days after the first wave of accounts was taken down, 19 new repositories appeared, and they were still online 8 days after the report. Researchers said the platform's scanning only checks for known malware and unsafe model formats, and the second wave's flagship model scanned as clean.

Pluto Security discovers malicious model repository activity on Hugging Face

Pluto Security discovered 31 malicious repositories on Hugging Face that masqueraded as small Qwen2 test checkpoints, using trust_remote_code to execute Python code before the model loaded, installing cryptocurrency mining programs and opening a reverse shell. The most-downloaded one, MENXIANGYU/qwen2-tiny, exceeded 28,000 downloads. Within days after the first wave of accounts was taken down, 19 new repositories appeared, and they were still online 8 days after the report. Researchers said the platform's scanning only checks for known malware and unsafe model formats, and the second wave's flagship model scanned as clean.

View story:Pluto Security discovers 31 malicious Hugging Face repositories

The US Department of Justice opens inquiry into Nvidia's acquisition of Hugging Face

The US Department of Justice has opened an inquiry into Nvidia's $12.93 billion acquisition of Hugging Face, assessing the deal's impact on AI competition, with the core focus being whether the transaction would give Nvidia control of a fundamental distribution point for AI models and software. Nvidia announced the acquisition on September 3, 2026, with consideration comprising approximately $11.9 billion in cash and up to $1 billion in employee retention equity incentives, and the deal is expected to close in the first half of 2027. Hugging Face hosts more than 3 million AI models and 500,000 datasets, serving over 18 million developers and 200,000 enterprises. According to Reuters, the investigation focuses on whether Nvidia could expand its market power and squeeze out competitors by controlling this important distribution channel for AI models; no specific antitrust violation has been identified so far.

Mistral releases Large 4 weights on Hugging Face

On October 29, 2026, Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter native multimodal model that continues reinforcement learning on the basis of the preview version, with improved coding and defensive cybersecurity capabilities. According to data published by Mistral AI, the model scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT Mistral Ecosystem Open-Weight license, and is priced at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output via API. On the same day, Unsloth released a 1-bit GGUF quantized version of the model, stating that it can run locally on a Mac Studio, retains 91% top-1 accuracy after quantization, and provides a download link and usage guide.

View story:Mistral releases Large 4 weights on Hugging Face