forked from phoenix-oss/llama-stack-mirror
## What does this PR do? Created a new math_500 open-benchmark based on OpenAI's [Let's Verify Step by Step](https://arxiv.org/abs/2305.20050) paper and hugging face's [HuggingFaceH4/MATH-500](https://huggingface.co/datasets/HuggingFaceH4/MATH-500) dataset. The challenge part of this benchmark is to parse the generated and expected answer and verify if they are same. For the parsing part, we refer to [Minerva: Solving Quantitative Reasoning Problems with Language Models](https://research.google/blog/minerva-solving-quantitative-reasoning-problems-with-language-models/). To simply the parse logic, as the next step, we plan to also refer to what [simple-eval](https://github.com/openai/simple-evals) is doing, using llm as judge to check if the generated answer matches the expected answer or not ## Test Plan on sever side, spin up a server with open-benchmark template `llama stack run llama_stack/templates/open-benchamrk/run.yaml` on client side, issue an open benchmark eval request `llama-stack-client --endpoint xxx eval run-benchmark "meta-reference-math-500" --model-id "meta-llama/Llama-3.3-70B-Instruct" --output-dir "/home/markchen1015/" --num-examples 20` and get ther aggregated eval results <img width="238" alt="Screenshot 2025-03-10 at 7 57 04 PM" src="https://github.com/user-attachments/assets/2c9da042-3b70-470e-a7c4-69f4cc24d1fb" /> check the generated answer and the related scoring and they make sense
230 lines
6.2 KiB
YAML
230 lines
6.2 KiB
YAML
version: '2'
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image_name: open-benchmark
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apis:
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- agents
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- datasetio
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- eval
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- inference
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- safety
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- scoring
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- telemetry
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- tool_runtime
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- vector_io
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providers:
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inference:
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- provider_id: openai
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provider_type: remote::openai
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config:
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api_key: ${env.OPENAI_API_KEY:}
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- provider_id: anthropic
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provider_type: remote::anthropic
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config:
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api_key: ${env.ANTHROPIC_API_KEY:}
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- provider_id: gemini
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provider_type: remote::gemini
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config:
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api_key: ${env.GEMINI_API_KEY:}
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- provider_id: groq
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provider_type: remote::groq
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config:
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url: https://api.groq.com
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api_key: ${env.GROQ_API_KEY:}
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- provider_id: together
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provider_type: remote::together
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config:
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url: https://api.together.xyz/v1
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api_key: ${env.TOGETHER_API_KEY:}
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vector_io:
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- provider_id: sqlite-vec
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provider_type: inline::sqlite-vec
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config:
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/sqlite_vec.db
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- provider_id: ${env.ENABLE_CHROMADB+chromadb}
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provider_type: remote::chromadb
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config:
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url: ${env.CHROMADB_URL:}
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- provider_id: ${env.ENABLE_PGVECTOR+pgvector}
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provider_type: remote::pgvector
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config:
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host: ${env.PGVECTOR_HOST:localhost}
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port: ${env.PGVECTOR_PORT:5432}
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db: ${env.PGVECTOR_DB:}
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user: ${env.PGVECTOR_USER:}
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password: ${env.PGVECTOR_PASSWORD:}
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safety:
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- provider_id: llama-guard
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provider_type: inline::llama-guard
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config: {}
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agents:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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persistence_store:
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type: sqlite
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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/agents_store.db
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telemetry:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
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sinks: ${env.TELEMETRY_SINKS:console,sqlite}
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sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/dev/trace_store.db}
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eval:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config: {}
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datasetio:
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- provider_id: huggingface
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provider_type: remote::huggingface
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config: {}
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- provider_id: localfs
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provider_type: inline::localfs
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config: {}
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scoring:
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- provider_id: basic
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provider_type: inline::basic
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config: {}
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- provider_id: llm-as-judge
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provider_type: inline::llm-as-judge
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config: {}
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- provider_id: braintrust
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provider_type: inline::braintrust
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config:
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openai_api_key: ${env.OPENAI_API_KEY:}
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tool_runtime:
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- provider_id: brave-search
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provider_type: remote::brave-search
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config:
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api_key: ${env.BRAVE_SEARCH_API_KEY:}
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max_results: 3
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- provider_id: tavily-search
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provider_type: remote::tavily-search
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config:
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api_key: ${env.TAVILY_SEARCH_API_KEY:}
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max_results: 3
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- provider_id: code-interpreter
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provider_type: inline::code-interpreter
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config: {}
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- provider_id: rag-runtime
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provider_type: inline::rag-runtime
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config: {}
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- provider_id: model-context-protocol
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provider_type: remote::model-context-protocol
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config: {}
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metadata_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/registry.db
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models:
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- metadata: {}
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model_id: openai/gpt-4o
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provider_id: openai
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provider_model_id: openai/gpt-4o
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model_type: llm
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- metadata: {}
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model_id: meta-llama/Llama-3.1-405B-Instruct
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provider_id: together
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provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
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model_type: llm
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- metadata: {}
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model_id: anthropic/claude-3-5-sonnet-latest
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provider_id: anthropic
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provider_model_id: anthropic/claude-3-5-sonnet-latest
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model_type: llm
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- metadata: {}
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model_id: gemini/gemini-1.5-flash
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provider_id: gemini
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provider_model_id: gemini/gemini-1.5-flash
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model_type: llm
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- metadata: {}
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model_id: meta-llama/Llama-3.3-70B-Instruct
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provider_id: groq
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provider_model_id: groq/llama-3.3-70b-versatile
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model_type: llm
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shields:
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- shield_id: meta-llama/Llama-Guard-3-8B
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vector_dbs: []
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datasets:
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- dataset_id: simpleqa
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provider_id: huggingface
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url:
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uri: https://huggingface.co/datasets/llamastack/simpleqa
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metadata:
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path: llamastack/simpleqa
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name:
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split: train
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dataset_schema:
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input_query:
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type: string
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expected_answer:
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type: string
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chat_completion_input:
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type: string
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- dataset_id: mmlu_cot
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provider_id: huggingface
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url:
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uri: https://huggingface.co/datasets/llamastack/mmlu_cot
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metadata:
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path: llamastack/mmlu_cot
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name: all
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split: test
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dataset_schema:
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input_query:
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type: string
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expected_answer:
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type: string
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chat_completion_input:
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type: string
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- dataset_id: gpqa_cot
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provider_id: huggingface
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url:
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uri: https://huggingface.co/datasets/llamastack/gpqa_0shot_cot
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metadata:
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path: llamastack/gpqa_0shot_cot
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name: gpqa_main
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split: train
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dataset_schema:
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input_query:
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type: string
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expected_answer:
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type: string
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chat_completion_input:
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type: string
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- dataset_id: math_500
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provider_id: huggingface
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url:
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uri: https://huggingface.co/datasets/llamastack/math_500
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metadata:
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path: llamastack/math_500
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name:
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split: test
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dataset_schema:
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input_query:
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type: string
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expected_answer:
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type: string
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chat_completion_input:
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type: string
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scoring_fns: []
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benchmarks:
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- benchmark_id: meta-reference-simpleqa
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dataset_id: simpleqa
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scoring_functions: ["llm-as-judge::405b-simpleqa"]
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- benchmark_id: meta-reference-mmlu-cot
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dataset_id: mmlu_cot
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scoring_functions: ["basic::regex_parser_multiple_choice_answer"]
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- benchmark_id: meta-reference-gpqa-cot
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dataset_id: gpqa_cot
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scoring_functions: ["basic::regex_parser_multiple_choice_answer"]
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- benchmark_id: meta-reference-math-500
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dataset_id: math_500
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scoring_functions: ["basic::regex_parser_math_response"]
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tool_groups:
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- toolgroup_id: builtin::websearch
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provider_id: tavily-search
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- toolgroup_id: builtin::rag
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provider_id: rag-runtime
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- toolgroup_id: builtin::code_interpreter
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provider_id: code-interpreter
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server:
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port: 8321
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