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Company: "moondream"
Mistral 3: Mistral Large 3 + Ministral 3B/8B/14B open weights models
mistral-large-3 ministral-3 clara-7b-instruct gen-4.5 claude-code mistral-ai anthropic apple runway moondream sparse-moe multimodality benchmarking open-source model-licensing model-performance long-context inference-optimization instruction-following local-inference code-generation model-integration anjney_midha _akhaliq alexalbert__ _catwu mikeyk
Mistral has launched the Mistral 3 family including Ministral 3 models (3B/8B/14B) and Mistral Large 3, a sparse MoE model with 675B total parameters and 256k context window, all under an Apache 2.0 open license. Early benchmarks rank Mistral Large 3 at #6 among open models with strong coding performance. The launch includes broad ecosystem support such as vLLM, llama.cpp, Ollama, and LM Studio integrations. Meanwhile, Anthropic acquired the open-source Bun runtime to accelerate Claude Code, which reportedly reached a $1B run-rate in ~6 months. Anthropic also announced discounted Claude plans for nonprofits and shared insights on AI's impact on work internally.
not much happened this weekend
claude-3.5-sonnet llama-3 llama-3-8b notebookllama min-omni-2 moondream openai anthropic hugging-face mistral-ai google-deepmind langchain deepmind microsoft pattern-recognition reinforcement-learning prompt-optimization text-to-speech model-optimization tensor-parallelism hyperparameters multimodal modal-alignment multimodal-fine-tuning ai-productivity privacy generative-ai rag retrieval-augmentation enterprise-text-to-sql amanda-askell philschmid stasbekman francois-fleuret mervenoyann reach_vb dzhng aravsrinivas sama lateinteraction andrew-y-ng bindureddy jerryjliu0
Moondream, a 1.6b vision language model, secured seed funding, highlighting a trend in moon-themed tiny models alongside Moonshine (27-61m ASR model). Claude 3.5 Sonnet was used for AI Twitter recaps. Discussions included pattern recognition vs. intelligence in LLMs, reinforcement learning for prompt optimization, and NotebookLlama, an open-source NotebookLM variant using LLaMA models for tasks like text-to-speech. Advances in model optimization with async-TP in PyTorch for tensor parallelism and hyperparameter tuning were noted. Mini-Omni 2 demonstrated multimodal capabilities across image, audio, and text for voice conversations with emphasis on modal alignment and multimodal fine-tuning. AI productivity tools like an AI email writer and LlamaCloud-based research assistants were introduced. Emphasis on practical skill development and privacy-conscious AI tool usage with Llama3-8B was highlighted. Generative AI tools such as #AIPythonforBeginners and GenAI Agents with LangGraph were shared. Business insights covered rapid execution in AI product development and emerging AI-related job roles. Challenges in enterprise-grade text-to-SQL and advanced retrieval methods were discussed with tutorials on RAG applications using LangChain and MongoDB.
CogVideoX: Zhipu's Open Source Sora
cogvideox llama-3-1 llama-3-405b moondream phi-3.5 llama-rank zhipu-ai alibaba meta-ai-fair google hugging-face nvidia togethercompute salesforce video-generation serverless-computing vision document-vqa text-vqa mixture-of-experts retrieval-augmented-generation long-context model-routing webgpu background-removal long-form-generation superposition-prompting rohanpaul_ai philschmid vikhyatk algo_diver jayalammar davidsholz
Zhipu AI, Alibaba's AI arm and China's 3rd largest AI lab, released the open 5B video generation model CogVIdeoX, which can run without GPUs via their ChatGLM web and desktop apps. Meta AI announced trust & safety research and CyberSecEval 3 alongside the release of Llama 3.1, with Llama 3 405B now available serverless on Google Cloud Vertex AI and Hugging Face x NVIDIA NIM API. Updates include Moondream, an open vision-language model improving DocVQA and TextVQA tasks, and the lightweight MoE chat model Phi-3.5 with 16x3.8B parameters. Together Compute introduced the Rerank API featuring Salesforce's LlamaRank model for document and code ranking. Research highlights include superposition prompting for RAG without fine-tuning, the AgentWrite pipeline for long-form content generation over 20,000 words, and a comparison showing Long Context methods outperform RAG at higher costs. Tools include Not Diamond, an AI model router, AI command line interfaces, and an open-source WebGPU background removal tool. "You don't even need GPUs to run it," referring to CogVIdeoX.
CodeLLama 70B beats GPT4 on HumanEval
codellama miqu mistral-medium llama-2-70b aphrodite-engine mixtral flatdolphinmaid noromaid rpcal chatml mistral-7b activation-beacon eagle-7b rwkv-v5 openhermes2.5 nous-hermes-2-mixtral-8x7b-dpo imp-v1-3b bakllava moondream qwen-vl meta-ai-fair ollama nous-research mistral-ai hugging-face ai-ethics alignment gpu-optimization direct-prompt-optimization fine-tuning cuda-programming optimizer-technology quantization multimodality context-length dense-retrieval retrieval-augmented-generation multilinguality model-performance open-source code-generation classification vision
Meta AI surprised the community with the release of CodeLlama, an open-source model now available on platforms like Ollama and MLX for local use. The Miqu model sparked debate over its origins, possibly linked to Mistral Medium or a fine-tuned Llama-2-70b, alongside discussions on AI ethics and alignment risks. The Aphrodite engine showed strong performance on A6000 GPUs with specific configurations. Role-playing AI models such as Mixtral and Flatdolphinmaid faced challenges with repetitiveness, while Noromaid and Rpcal performed better, with ChatML and DPO recommended for improved responses. Learning resources like fast.ai's course were highlighted for ML/DL beginners, and fine-tuning techniques with optimizers like Paged 8bit lion and adafactor were discussed.
At Nous Research AI, the Activation Beacon project introduced a method for unlimited context length in LLMs using "global state" tokens, potentially transforming retrieval-augmented models. The Eagle-7B model, based on RWKV-v5, outperformed Mistral in benchmarks with efficiency and multilingual capabilities. OpenHermes2.5 was recommended for consumer hardware due to its quantization methods. Multimodal and domain-specific models like IMP v1-3b, Bakllava, Moondream, and Qwen-vl were explored for classification and vision-language tasks. The community emphasized centralizing AI resources for collaborative research.