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Deploy full fine-tuned TinyLlama and fix retrieval recall
Model: - Replace the LoRA-merged-and-quantized model with the full fine-tune from finetune_tinylama_full.ipynb, converted to GGUF and quantized to Q4_K_M (llama.cpp), committed via git-lfs as tinyllama_dop_full_q4_k_m.gguf (636MB). No separate model-repo upload needed; Dockerfile now COPYs it directly instead of curling it from a separate HF model repo at build time. - Added MODEL_BACKEND=hf as an additional (non-default) option to serve a raw transformers checkpoint instead of GGUF, for future local testing on GPU hardware; the deployed default stays MODEL_BACKEND=gguf, matching this Space's existing CPU-only setup. - Added PROMPT_TEMPLATE (default: inst), decoupled from MODEL_BACKEND. The full fine-tune was trained on Llama/Mistral-style "<s>[INST] ... [/INST]" pairs, not the old Zephyr-style <|system|>/<|user|>/<|assistant|> template this Space used for the LoRA model - prompt format now follows which model is served, not which library runs it. - Fixed a do…68e6652
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updated dockerfile
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updated to granular context chunks
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updates
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updated docker
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updated
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Updated to phi1.5
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updated dockerfile for quantized
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revised setup2
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Implemented dynamic DB build and other app/docker changes
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25july upload
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