Search “Hugging Face”

gentic coding and cybersecurity. The model ranks first among open models on Cognition's FrontierCode and sets a new benchmark for Pareto efficiency on Artificial Analysis; on cybersecurity it claims state-of-the-art, having been tested by vendors including CrowdStrike, Cloudflare, Rubrik, and Zscaler. The weights can be downloaded on Hugging Face and customized and deployed with Mistral Forge. Arthur Mensch reposted and said progress over recent weeks has been good, and now the weights are open.

Matched fieldsSummary

testing by customers and cybersecurity partners and further training with reinforcement learning, ML4's success rate on Terminal-Bench 4.0 rose from 28% to 41%, and two new reasoning effort levels, Low and Medium, were added. Mistral research lead Pierre Stock said that at equivalent capability the new version uses about 50% fewer tokens than the preview; the weights became available for download on Hugging Face at 19:00 and are also offered through the Mistral API and the coding assistant Vibe.

Matched fieldsSummary、Body

ng. The model is an open-weight 30B MoE architecture that activates approximately 4B parameters during inference, can specify via text instructions whether to retain filler words and how numbers are written, and supports speaker diarization for up to 8 people and bidirectional Japanese-English translation. The weights are released on Hugging Face as NVIDIA-Nemotron-3.5-Transcribe-30B-A4B under the OpenMDW-1.1 license, with BF16, FP8, and NVFP4 versions and NeMo Speech fine-tuning tools provided.

Matched fieldsSummary、Body

Mistral released the weights of its frontier open-source model Mistral Large 4 (ML4) on Hugging Face on Thursday; the model had only been available in preview three weeks earlier. Mistral calls ML4 "the world's best open-source agentic coding model," scoring 41% on Terminal-Bench 4.0, slightly above GLM-5.3's 40%, and says it uses an average of 47% fewer tokens per task than GLM-5.3; it scored 79% on the CyberGym E2E defensive cybersecurity test, compared with 29% for GLM-5.3. Mistral Vice Presi

Matched fieldsSummary、Body

Mistral AI released the open weights of Mistral Large 4 (codename Le Chonk) on Hugging Face on October 29. The model has 100 billion parameters out of a total parameter scale of the 100-billion class, with about 4.9 billion parameters actually invoked per token, using a MoE architecture with 128 experts, of which 4 are activated each time plus 1 shared expert. Compared with the October 6 preview version, Mistral says it continued reinforcement learning over three weeks, raising the Terminal-Benc

Matched fieldsSummary、Body

Mistral has released the weights of Mistral Large 4 on Hugging Face. The model, nicknamed Le Chonk, has approximately 1 trillion parameters with 52 billion activated per step, and supports text and image inputs. On Terminal-Bench 4.0, its score rose from 28% in the preview version to 41%, slightly above GLM-5.3's 40%, but on the new version of the Artificial Analysis Intelligence Index it scored only 26 points, below the 32 points of DeepSeek V4.1 Flash and GLM-5.3. API pricing is $0.70 per mill

Matched fieldsSummary、Body

nglish version Parakeet Unified, and Alibaba Qwen3-ASR (0.6B/1.7B, covering 30 languages and 22 Chinese dialects). These models have been converted to Core ML and published on the Hugging Face Hub; Parakeet takes about 20–40 seconds to transcribe 1 hour of audio, while Qwen3-ASR is slower and requires several GB of memory. The package also adds a speaker diarization module, SpeakerDiarizer, which the Apple framework lacks; it is based on NVIDIA Streaming Sortformer and can track up to 4 speakers

Matched fieldsSummary、Body

ut $17.1 billion, and its gross margin guidance dropped from 43% to 38%; its stock price plunged about 21% in after-hours trading on October 29. Anthropic filed for a Nasdaq IPO on October 26 (ticker ANT), with revenue of $16.286 billion in the first half of 2026, up about 1,300% year over year, and second-quarter operating profit of $974 million. The U.S. Department of Justice has opened inquiries into whether Nvidia's acquisition of Hugging Face for about $12.93 billion affects AI competition.

Matched fieldsSummary、Body

On October 29, Baseten released joey-1, a 2B-parameter decision model, priced at $0.01 per million tokens. It is now available on Baseten Model APIs and has open weights on Hugging Face. The model was post-trained by the in-house lab Base Labs based on Qwen3.5-2B, and shares the same data recipe and API with mammoth-1 (27B) and elephant-1 (9B), so switching requires only a one-line change. The model card shows that joey-1 scored 63.30 on the public Jev Decision Index 0.4 leaderboard, ranking 37t

Matched fieldsSummary、Body

720p resolutions; the 5-billion-parameter version can achieve real-time generation at over 24 frames per second at 720p on a single consumer-grade RTX 5090. The open-source release includes the 5-billion and 14-billion parameter model weights, real-time video generation code, the EdenGolem v0.1 skill for converting text into simple games, and a complete prompt-to-game front-end toolchain; the code is released on GitHub under the Apache 2.0 license, and the weights are available on Hugging Face.

Matched fieldsSummary、Body

s), at a price of $0.01 per million tokens, one-tenth that of mammoth-1. joey-1 is post-trained from Qwen3.5-2B and shares the same data recipe and API with elephant-1 and mammoth-1, so switching requires changing only one line. The model is now available on Baseten Model APIs, with zero data retention by default, and its weights are open-sourced on Hugging Face; Model APIs supports a 16k context window, while dedicated deployments can unlock a 262k context window and support fine-tuned weights.

Matched fieldsSummary、Body

ter open-source model Mistral Large 4 (ML4, nicknamed Le Chonk), which enterprises and government agencies can download and deploy themselves; the weights are already available on Hugging Face and via its API. Mistral says the model's performance is comparable to that of the open-source model GLM-5.3, but it consumes 47% fewer tokens per task, and it ranks first among open-source models on Artificial Analysis's Coding Agent Index. Pierre Stock, Mistral's Vice President of Science, said that open

Matched fieldsSummary、Body

brated probabilities instead of generating text, targeting high-concurrency routing, classification, and guardrail checks. The model scores 63.30 on the public Jev Decision Index 0.4 set, the highest among models under 4B, but 8.94 points lower than the 9B elephant-1. Model APIs are priced at $0.01 per million input tokens, with a P50 latency of 20 ms for an average 2000-token request; the weights are open-sourced under the MIT license on Hugging Face and can be self-hosted on a single CUDA GPU.

Matched fieldsSummary、Body

or download after ending its public preview. The model supports text and image processing, with a focus on strengthening defensive cybersecurity, agentic capabilities, and geospatial and grounding capabilities, and is said to achieve state-of-the-art results on agentic benchmarks such as SWE-bench and Terminal Bench. The model was trained on more than 160 languages and is available via Mistral Studio, the Mistral API, OpenRouter, Hugging Face open weights, Vibe Code, and Nvidia-hosted endpoints.

Matched fieldsSummary、Body

Nvidia has released the open-weight multilingual transcription model Nemotron 3.5 Transcribe, which ranks first on the Hugging Face Open ASR English open-source model leaderboard with an average word error rate of 5.33%. The model uses a 30B MoE architecture with 4B activated parameters, supports transcription in 40 languages and translation between 31 languages and English, can label speakers and distinguish up to 8 voices, handles recordings up to 1 hour long, but does not support real-time st

Matched fieldsSummary、Body

NVIDIA has released the open-weight model Nemotron 3.5 Transcribe on Hugging Face and ModelScope, integrating transcription, speaker diarization, translation, and audio question answering into a single checkpoint. It supports transcription in 40 languages, translation between 31 languages and English, and speaker labeling in 13 languages, can process recordings up to 1 hour long, and can label up to 8 speakers. The model ranks first among open-weight models on the English leaderboard of the Hugg

Matched fieldsSummary、Body

oice model Nemotron 3.5 Transcribe, support for Liquid AI's LFM3 model family, and a control in the NVIDIA App that can allocate GPU resources between foreground applications and background agents. Nemotron 3.5 Transcribe supports transcription, speaker labeling, and translation in 40 languages, has approximately 4.3 billion active parameters, and is now available on Hugging Face. RTX Spark laptops are now shipping from ASUS, Dell, HP, Lenovo, Microsoft, and MSI, with a starting price of $2,599.

Matched fieldsSummary、Body

Matched fieldsSummary、Body

ltaneously launched version 2 of the open speech dataset Granary. The model supports translation between 31 languages and English, processes recordings up to one hour long, and distinguishes up to 8 speakers; Nvidia claims it outperforms competing products on benchmarks such as the Hugging Face Open ASR Leaderboard. The model has 30 billion parameters and can run on Jetson AGX Thor, DGX Spark, and Blackwell data center chips, but it only supports recorded audio and does not support live streams.

Matched fieldsSummary、Body

SoMark has open-sourced OXR-1.0, a 1.3B-parameter document parsing model that converts PDFs and page images into Markdown or JSON. The weights have been released on Hugging Face, and lightweight testing can run on a single 24GB GPU. The model ranks first on olmOCR-Bench, OmniDocBench, and DocParsingBench, achieves a decoding speed of 558 tokens/s on a single NVIDIA RTX PRO 6000 GPU, and parses 100 pages end-to-end in about 101 seconds. Its post-training stage employs a recursive self-improvement

Matched fieldsSummary、Body