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Phase 2: Add Devstral backend support
gary-boon +1: Claude Opus 4.5Add support for Mistral-based models (Devstral) with: - MistralAdapter in model_adapter.py for Mistral architecture - devstral-small config (40 layers, 32 Q heads, 8 KV heads, 131K vocab) - Percentage-based layer classification (works for any layer count) - Environment variable support: - DEFAULT_MODEL: which model to load (default: codegen-350m) - TORCH_DTYPE: bf16/fp16/fp32 (default: auto based on device) - MAX_CONTEXT: context length limit (default: 8192) - BATCH_SIZE: batch size (default: 1) - GET /models endpoint: list available models with hardware availability - GET /models/current endpoint: return currently loaded model info Devstral requires TORCH_DTYPE=bf16 when deployed. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>9080f28
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Add Code Llama 7B support with hardware-aware filtering and ICL timeout fixes
gary-boon +1: Claude- Added multi-architecture support to ICL components (attention extractor, service, induction detector) - Implemented hardware-aware model filtering for CPU/GPU spaces - Fixed Code Llama tokenizer padding token configuration - Updated model config with accurate Code Llama 7B specifications - Added model adapter pattern for seamless architecture switching 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>ed40a9a