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(docs) fast eval with proxy
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@ -243,6 +243,62 @@ python gpt4_eval.py -q '../evaluation_set/flask_evaluation.jsonl'
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```
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</TabItem>
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<TabItem value="FastEval" label="Fast Eval">
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**Step 1: Start the local proxy**
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see supported models [here](https://docs.litellm.ai/docs/simple_proxy)
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```shell
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$ litellm --model huggingface/bigcode/starcoder
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```
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**Step 2: Set OpenAI API Base & Key**
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```shell
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$ export OPENAI_API_BASE=http://0.0.0.0:8000
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```
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Set this to anything since the proxy has the credentials
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```shell
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export OPENAI_API_KEY=anything
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```
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**Step 3 Run with FastEval**
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**Clone FastEval**
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```shell
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# Clone this repository, make it the current working directory
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git clone --depth 1 https://github.com/FastEval/FastEval.git
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cd FastEval
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```
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**Set API Base on FastEval**
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On FastEval make the following **2 line code change** to set `OPENAI_API_BASE`
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https://github.com/FastEval/FastEval/pull/90/files
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```python
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try:
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api_base = os.environ["OPENAI_API_BASE"] #changed: read api base from .env
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if api_base == None:
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api_base = "https://api.openai.com/v1"
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response = await self.reply_two_attempts_with_different_max_new_tokens(
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conversation=conversation,
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api_base=api_base, # #changed: pass api_base
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api_key=os.environ["OPENAI_API_KEY"],
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temperature=temperature,
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max_new_tokens=max_new_tokens,
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```
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**Run FastEval**
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Set `-b` to the benchmark you want to run. Possible values are `mt-bench`, `human-eval-plus`, `ds1000`, `cot`, `cot/gsm8k`, `cot/math`, `cot/bbh`, `cot/mmlu` and `custom-test-data`
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Since LiteLLM provides an OpenAI compatible proxy `-t` and `-m` don't need to change
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`-t` will remain openai
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`-m` will remain gpt-3.5
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```shell
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./fasteval -b human-eval-plus -t openai -m gpt-3.5-turbo
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```
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</TabItem>
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<TabItem value="mlflow" label="ML Flow Eval">
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MLflow provides an API `mlflow.evaluate()` to help evaluate your LLMs https://mlflow.org/docs/latest/llms/llm-evaluate/index.html
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