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42 lines
1.2 KiB
Markdown
42 lines
1.2 KiB
Markdown
# (Experimental) LLama Stack UI
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## Docker Setup
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:warning: This is a work in progress.
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## Developer Setup
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1. Start up Llama Stack API server. More details [here](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html).
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```
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llama stack build --template together --image-type conda
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llama stack run together
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```
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2. (Optional) Register datasets and eval tasks as resources. If you want to run pre-configured evaluation flows (e.g. Evaluations (Generation + Scoring) Page).
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```bash
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$ llama-stack-client datasets register \
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--dataset-id "mmlu" \
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--provider-id "huggingface" \
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--url "https://huggingface.co/datasets/llamastack/evals" \
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--metadata '{"path": "llamastack/evals", "name": "evals__mmlu__details", "split": "train"}' \
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--schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string", "chat_completion_input": {"type": "string"}}}'
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```
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```bash
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$ llama-stack-client eval_tasks register \
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--eval-task-id meta-reference-mmlu \
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--provider-id meta-reference \
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--dataset-id mmlu \
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--scoring-functions basic::regex_parser_multiple_choice_answer
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```
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3. Start Streamlit UI
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```bash
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cd llama_stack/distribution/ui
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pip install -r requirements.txt
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streamlit run app.py
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```
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