Mistral AI (open models)
Mistral’s open-weight checkpoints (e.g. 7B era, Mixtral MoE) and Apache-2.0–licensed **code** alongside proprietary flagship lines—verify each checkpoint.
Why it is included
European-led open-weights story with strong instruction and MoE releases used across the stack.
Best for
Teams wanting permissively licensed smaller models or MoE under clear Apache weights where offered.
Strengths
- Mixtral MoE
- Strong OSS tooling alignment
- HF presence
Limitations
- Model mix is not uniformly open—read each card
Good alternatives
Meta Llama · Qwen · Gemma
Related tools
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vLLM
High-throughput LLM serving with PagedAttention, continuous batching, and OpenAI-compatible APIs for GPU clusters.
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Hugging Face Transformers
State-of-the-art pretrained models for PyTorch, TensorFlow, and JAX.
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Meta Llama (open models)
Meta’s Llama family of open **weights** (subject to Llama license) with reference code, tooling, and downloads via Hugging Face and meta-llama org.
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Qwen
Alibaba’s Qwen family (dense and MoE) with strong multilingual and coding variants; weights and code on Hugging Face under stated licenses per release.
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DeepSeek
DeepSeek open-weight models (e.g. V3/R1 lineage) with MIT or custom terms per release—high capability coding and reasoning checkpoints.
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Google Gemma
Google’s smaller open **weights** Gemma line (Gemma 2/3, etc.) with Gemma license terms, plus `gemma.cpp` for lightweight CPU inference.