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Company: "unsloth"
Qwen3.5-397B-A17B: the smallest Open-Opus class, very efficient model
qwen3.5-397b-a17b qwen3.5-plus qwen3-max qwen3-vl kimi alibaba openai deepseek z-ai minimax kimi unsloth ollama vllm native-multimodality spatial-intelligence sparse-moe long-context model-quantization model-architecture model-deployment inference-optimization apache-2.0-license pete_steinberger justinlin610
Alibaba released Qwen3.5-397B-A17B, an open-weight model featuring native multimodality, spatial intelligence, and a hybrid linear attention + sparse MoE architecture supporting 201 languages and long context windows up to 256K tokens. The model shows improvements over previous versions like Qwen3-Max and Qwen3-VL, with a sparsity ratio of about 4.3%. Community discussions highlighted the Gated Delta Networks enabling efficient inference despite large model size (~800GB BF16), with successful local runs on Apple Silicon using quantization techniques. The hosted API version, Qwen3.5-Plus, supports 1M context and integrates search and code interpreter features. This release follows other Chinese labs like Z.ai, Minimax, and Kimi in refreshing large models. The model is licensed under Apache-2.0 and is expected to be the last major release before DeepSeek v4. The news also notes Pete Steinberger joining OpenAI.
not much happened today
qwen-image-2512 ax-k1 k-exaone sk-telecom lg upstage naver alibaba unsloth replicate mixture-of-experts model-release quantization open-source-models image-generation model-integration model-benchmarking compute-costs dataset-curation eliebakouch clementdelangue dorialexander rising_sayak _akhaliq ostrisai ivanfioravanti yupp_ai
South Korea's Ministry of Science launched a coordinated program with 5 companies to develop sovereign foundation models from scratch, featuring large-scale MoE architectures like SK Telecom A.X-K1 (519B total / 33B active) and LG K-EXAONE (236B MoE / 23B active), with a total first-round budget of ~$140M. This initiative contrasts with EU approaches by focusing funding on fewer stakeholders and explicitly budgeting for data. Meanwhile, Alibaba's Qwen-Image-2512 emerges as a leading open-source image generation model, rapidly integrated into various toolchains including AI-Toolkit and local inference paths with quantization support, and hosted on platforms like Replicate. The model has undergone extensive blind testing with over 10,000 rounds on AI Arena, highlighting its ecosystem adoption.
Western Open Models get Funding: Cohere $500m @ 6.8B, AI2 gets $152m NSF+NVIDIA grants
gpt-5 o3 command-a gemma-3-270m imagen-4 dinov3 openai perplexity-ai ai2 nvidia cohere meta-ai-fair google hugging-face ollama unsloth model-speed funding ai-infrastructure on-device-ai quantization embedding-models image-generation self-supervised-learning vision dense-prediction benchmarking instruction-following model-optimization model-release challenge joelle_pineau fchollet awnihannun _philschmid osanseviero
OpenAI's GPT-5 achieved a speedrun of Pokemon Red 3x faster than o3. Perplexity raised $200M at a $20B valuation. AI2 secured $75M NSF grants and $77M from NVIDIA for AI infrastructure projects like Olmo and Molmo. Cohere raised $500M and hired Joelle Pineau from meta-ai-fair, boosting models like Command A. Google released the Gemma 3 270M on-device tiny LLM with INT4 QAT checkpoints and large embedding tables, and made Imagen 4 generally available with a fast version at $0.02/image. Meta-ai-fair introduced DINOv3, a family of self-supervised vision foundation models with high-resolution dense features and strong performance on benchmarks like COCO detection and ADE20K segmentation, under a permissive license. A $150,000 MiniMax AI Agent Challenge is ongoing with 200+ prizes, encouraging AI project builds by August 25.
ALL of AI Engineering in One Place
claude-3-sonnet claude-3 openai google-deepmind anthropic mistral-ai cohere hugging-face adept midjourney character-ai microsoft amazon nvidia salesforce mastercard palo-alto-networks axa novartis discord twilio tinder khan-academy sourcegraph mongodb neo4j hasura modular cognition anysphere perplexity-ai groq mozilla nous-research galileo unsloth langchain llamaindex instructor weights-biases lambda-labs neptune datastax crusoe covalent qdrant baseten e2b octo-ai gradient-ai lancedb log10 deepgram outlines crew-ai factory-ai interpretability feature-steering safety multilinguality multimodality rag evals-ops open-models code-generation gpus agents ai-leadership
The upcoming AI Engineer World's Fair in San Francisco from June 25-27 will feature a significantly expanded format with booths, talks, and workshops from top model labs like OpenAI, DeepMind, Anthropic, Mistral, Cohere, HuggingFace, and Character.ai. It includes participation from Microsoft Azure, Amazon AWS, Google Vertex, and major companies such as Nvidia, Salesforce, Mastercard, Palo Alto Networks, and more. The event covers 9 tracks including RAG, multimodality, evals/ops, open models, code generation, GPUs, agents, AI in Fortune 500, and a new AI leadership track. Additionally, Anthropic shared interpretability research on Claude 3 Sonnet, revealing millions of interpretable features that can be steered to modify model behavior, including safety-relevant features related to bias and unsafe content, though more research is needed for practical applications. The event offers a discount code for AI News readers.
Fixing Gemma
gemma claude-3-opus claude-3 mistral-large gpt-4 google unsloth anthropic mistral-ai finetuning numerical-precision benchmarking structured-data-extraction adaptive-equalizer information-theory hallucination-detection model-stability daniel-han yann-lecun francois-chollet arav-srinivas _aidan_clark_
Google's Gemma model was found unstable for finetuning until Daniel Han from Unsloth AI fixed 8 bugs, improving its implementation. Yann LeCun explained technical details of a pseudo-random bit sequence for adaptive equalizers, while François Chollet discussed the low information bandwidth of the human visual system. Arav Srinivas reported that Claude 3 Opus showed no hallucinations in extensive testing, outperforming GPT-4 and Mistral-Large in benchmarks. Reflections from Yann LeCun highlight ongoing AI progress toward human-level intelligence. The community is shifting pipelines to work better with Claude models, and emotional experiences in ML development were shared by Aidan Clark.