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Thursday

  1. UnslothSelected

    Unsloth releases the Dynamic GGUF quantized version of Flint, runnable locally on 24GB of memory

    Unsloth has released the Dynamic GGUF quantized version of Flint, which can run locally on 24GB-memory devices, along with a running guide. Flint is Recursive's Apache-2.0 model with 39.8B parameters and 2.5B active, which Unsloth calls the strongest MoE model under 50B parameters, reaching 125 t/s on Mac and 150 t/s on RTX Spark. Recursive co-founder Richard Socher previously introduced that the model was built from scratch in under 4 months by the automated research system Eureka Machine, and the weights are now open on Hugging Face.

    ntized version of Flint, which can run locally on 24GB-memory devices, along with a running guide. Flint is Recursive's Apache-2.0 model with 39.8B parameters and 2.5B active, which Unsloth calls the strongest MoE model under 50B parameters, reaching 125 t/s on Mac and 150 t/s on RTX Spark. Recursive co-founder Richard Socher previously introduced that the model was built from scratch in under 4 months by the automated research system Eureka Machine, and the weights are now open on Hugging Face.

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    #company#English text is being prepared.#English text is being prepared.#English text is being prepared.
  2. UCSD Hao AI Lab

    FastVideo FastH3 Omni Runs Locally on DGX Spark, RTX GPUs, and Apple MLX

    Hao AI Lab announced that FastVideo FastH3 Omni can run locally on NVIDIA DGX Spark, RTX GPUs, and Apple MLX. Generating a 5-second 480p video from a single photo and a prompt takes about 3 minutes, or about 2 minutes on two Sparks; FastH3 T2AV takes 52.6 seconds on a single Spark and 30.7 seconds on two Sparks. The clips in the demo video were all generated by DGX Spark using an 8-step NVFP4 checkpoint, and blog, Cookbook, and Hugging Face links are provided.

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    #lab#Product updates#Video#Open-source ecosystem
  3. Thomas Wolf

    Thomas Wolf comments on Recursive's new model Flint: quantizing lm_head to 4 bits still comes close to bf16

    Hugging Face co-founder Thomas Wolf said that his AGI standard remains AI winning a Nobel Prize, but the AI researchers at Recursive quantized lm_head—which is usually advised to be kept at high precision—to 4 bits, and the result differs from bf16 by only 0.7 percentage points, which impressed him. He also mentioned that the model was released as open weights on Hugging Face, and congratulated Recursive. The quoted original Recursive post introduced that Flint is its first model, positioned as a fast local coding model, built by the automated research system Eureka Machine, reaching 125+ tokens/sec on a MacBook Pro and 150+ on RTX Spark, and built from scratch in under 4 months.

    Hugging Face co-founder Thomas Wolf said that his AGI standard remains AI winning a Nobel Prize, but the AI researchers at Recursive quantized lm head—which is usually advised to be kept at high precision—to 4 bits, and the result differs from bf16 by only 0.7 percentage points, which impressed him. He also mentioned that the model was released as open weights on Hugging Face, and congratulated Recursive. The quoted original Recursive post introduced that Flint is its first model, positioned as

    Matched fieldsSummary

    #editorial#English text is being prepared.#Open-source ecosystem#English text is being prepared.
  4. Rohan PaulSelected

    Recursive releases Flint, a 40B MoE coding model, at 125+ tokens/s on a MacBook Pro

    Recursive has released its first model, Flint, a 40B mixture-of-experts coding model under the Apache 2.0 license, with weights now open on Hugging Face, built from scratch in under 4 months by the automated research system Eureka Machine. Flint generates code at 125+ tokens/s on a MacBook Pro and reaches 150+ tokens/s on an RTX Spark. On a 48GB MacBook, its average quality is about 81%, compared with about 85% for Qwen3.8 27B, but in 128K-token long sessions it is about 7 times faster than the latter.

    Recursive has released its first model, Flint, a 40B mixture-of-experts coding model under the Apache 2.0 license, with weights now open on Hugging Face, built from scratch in under 4 months by the automated research system Eureka Machine. Flint generates code at 125+ tokens/s on a MacBook Pro and reaches 150+ tokens/s on an RTX Spark. On a 48GB MacBook, its average quality is about 81%, compared with about 85% for Qwen3.8 27B, but in 128K-token long sessions it is about 7 times faster than the

    Matched fieldsSummary

    #ai-news#Model releases#English text is being prepared.#English text is being prepared.
  5. TechCrunch AI

    Recursive releases laptop-class coding model Flint, says it was built mainly with AI involvement

    AI startup Recursive has released its first model, Flint, a coding model that runs offline on a laptop and is freely downloadable on Hugging Face under the Apache 2.0 license; the company says its development process was mainly carried out with the involvement of its in-house Eureka Machine. Flint is an MoE model that activates only 2.5B of its 40B parameters at a time, generates at about 131 tokens/sec on a MacBook Pro with an M4 Pro, and outperforms other models under 50B parameters on SWE-Bench Verified. Recursive says Flint requires more guidance than large paid coding assistants and recommends breaking large tasks into small steps.

    AI startup Recursive has released its first model, Flint, a coding model that runs offline on a laptop and is freely downloadable on Hugging Face under the Apache 2.0 license; the company says its development process was mainly carried out with the involvement of its in-house Eureka Machine. Flint is an MoE model that activates only 2.5B of its 40B parameters at a time, generates at about 131 tokens/sec on a MacBook Pro with an M4 Pro, and outperforms other models under 50B parameters on SWE-Ben

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    #tech-media#Model releases#English text is being prepared.#English text is being prepared.

Wednesday

  1. Modal Official Engineering BlogSelected

    Modal Releases LLM Training Library Modal Dojo

    Modal has released Modal Dojo, a training library that provides tuned default recipes, modifiable parameters, and an out-of-the-box metrics observability dashboard, supporting models from 500 million to trillions of parameters. The library is built on open-source frameworks such as Miles and Slime, can load datasets from Hugging Face, and lets agents query run status, logs, and trajectories via a CLI. Axiom Bio says it has used it to launch training of DeepSeek V4.1 Flash with a 64k context, running on 64 B300s.

    d Modal Dojo, a training library that provides tuned default recipes, modifiable parameters, and an out-of-the-box metrics observability dashboard, supporting models from 500 million to trillions of parameters. The library is built on open-source frameworks such as Miles and Slime, can load datasets from Hugging Face, and lets agents query run status, logs, and trajectories via a CLI. Axiom Bio says it has used it to launch training of DeepSeek V4.1 Flash with a 64k context, running on 64 B300s.

    Matched fieldsSummary、Body

    #company#Product updates#English text is being prepared.#Open-source ecosystem
  6. Unite.AI

    NVIDIA Releases Nemotron 3.5 Series of Open Models, Covering Embedding, Reranking, and Document Parsing

    On October 21, 2026, NVIDIA expanded the Nemotron Retriever series, launching Nemotron 3.5 Embed (8B BF16, 1B BF16, 1B NVFP4), Nemotron 3.5 Rerank 8B BF16, and the 631-million-parameter Nemotron 3.5 Parse, aimed at multimodal retrieval and document processing for enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retrieval accuracy. Parse scores 75.20 on ParseBench and processes 10.5 pages per second on a single H100; the open weights, data, and training recipes have been released on Hugging Face and can be deployed via NVIDIA NIM as well as Baseten, DeepInfra, and FriendliAI.

    enterprise agent workflows. NVIDIA says that adding Rerank to the agent retrieval pipeline reduces the token cost per query by about 14% (with the 8B embedding model) and 13% (with the 1B model), respectively, while also improving retrieval accuracy. Parse scores 75.20 on ParseBench and processes 10.5 pages per second on a single H100; the open weights, data, and training recipes have been released on Hugging Face and can be deployed via NVIDIA NIM as well as Baseten, DeepInfra, and FriendliAI.

    Matched fieldsSummary、Body

    #hot-signal#Model releases#RAG#Multimodal
  9. PyTorch BlogSelected

    Google and Meta jointly launch TorchTPU, enabling PyTorch to run natively on TPUs

    Google and Meta's PyTorch teams have collaborated to launch the open-source project TorchTPU, providing a native PyTorch backend for Google TPUs; migration only requires changing model.to("cuda") to model.to("tpu"). The project supports both eager and torch.compile modes, adds a new DeferAndFuse eager mode, covers about 450 Aten operators, and has been verified to run out of the box on 4,917 Hugging Face models, with a Tier 1 pass rate of 98% and Tier 2 numerical consistency relative to GPUs of 97%. According to the official announcement, it has already been used in the inference and training production workloads of some major Google Cloud customers, and is integrated with vLLM, SGLang, TorchTitan, Ray, and TorchRec.

    The project supports both eager and torch.compile modes, adds a new DeferAndFuse eager mode, covers about 450 Aten operators, and has been verified to run out of the box on 4,917 Hugging Face models, with a Tier 1 pass rate of 98% and Tier 2 numerical consistency relative to GPUs of 97%. According to the official announcement, it has already been used in the inference and training production workloads of some major Google Cloud customers, and is integrated with vLLM, SGLang, TorchTitan, Ray, an

    Matched fieldsSummary、Body

    #engineering#Product updates#Open-source ecosystem#English text is being prepared.

Tuesday

  2. Hugging FaceSelected

    ALTK-Evolve Uses Learned Memory to Put GPT-4.1 Customer-Service Agent Atop the τ²-bench Retail Leaderboard

    IBM Research has released ALTK-Evolve on Hugging Face. It uses GPT-4.1 to distill behavioral guidelines from the agent's own training trajectories and append them to the system prompt, achieving 67.5% Pass^4 on the τ²-bench retail task and surpassing all retail entries in its October 12, 2026 leaderboard snapshot. The method requires no weight updates or human annotation. Retail average accuracy rises from 78.1% to 82.5%, and Pass^4 rises from 57.5% to 67.5%; in the airline domain, Pass^4 rises from 35.0% to 45.0% with GPT-4.1 and to 82.0% with GPT-5.5. The authors say the gains come from more accurate decisions rather than longer conversations; the guidelines slightly reduce the number of dialogue turns, and the added cost is mainly in input tokens.

    IBM Research has released ALTK-Evolve on Hugging Face. It uses GPT-4.1 to distill behavioral guidelines from the agent's own training trajectories and append them to the system prompt, achieving 67.5% Pass^4 on the τ²-bench retail task and surpassing all retail entries in its October 12, 2026 leaderboard snapshot. The method requires no weight updates or human annotation. Retail average accuracy rises from 78.1% to 82.5%, and Pass^4 rises from 57.5% to 67.5%; in the airline domain, Pass^4 rises

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    #lab#Papers/Research#Agents#Evaluations / benchmarks
  5. Artificial Analysis

    Ant Group's Ming-3.0-tts Tops the Open-Source TTS Leaderboard

    The Ming-3.0-tts model from Ant Group's LingAGI team has become the top-ranked open-weight model in the Artificial Analysis Controlled Speech TTS Arena, and with a pronunciation robustness score of 93.2% it is tied for first among 31 models, level with Fish Audio Drama 3 Preview and ahead of Eleven v4's 91.7%. The model has 0.5B dense parameters, supports preset voices and stateless voice cloning, and its weights are released on Hugging Face under the MIT license; there is also a 7.9B MoE variant, Ming-3.0-tts-tiny. Its Controlled Speech Elo is 1,069, ranking first among open-source models and 13th overall; its Provider Voice Elo is 1,188, ranking third among open-source models and 19th overall.

    it is tied for first among 31 models, level with Fish Audio Drama 3 Preview and ahead of Eleven v4's 91.7%. The model has 0.5B dense parameters, supports preset voices and stateless voice cloning, and its weights are released on Hugging Face under the MIT license; there is also a 7.9B MoE variant, Ming-3.0-tts-tiny. Its Controlled Speech Elo is 1,069, ranking first among open-source models and 13th overall; its Provider Voice Elo is 1,188, ranking third among open-source models and 19th overall.

    Matched fieldsSummary、Body

    #editorial#Evaluations / benchmarks#Speech#Open-source ecosystem

Monday