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(docs) simple proxy

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@ -237,169 +237,27 @@ $ litellm --model command-nightly
## Using with OpenAI compatible projects ## Using with OpenAI compatible projects
LiteLLM allows you to set `openai.api_base` to the proxy server and use all LiteLLM supported LLMs in any OpenAI supported project Set `base_url` to the LiteLLM Proxy server
<Tabs> <Tabs>
<TabItem value="openai" label="OpenAI v1.0.0+">
<TabItem value="flask evals" label="FLASK Evals">
FLASK - Fine-grained Language Model Evaluation
Use litellm to evaluate any LLM on FLASK https://github.com/kaistAI/FLASK
**Step 1: Start the local proxy**
```shell
$ litellm --model huggingface/bigcode/starcoder
```
**Step 2: Set OpenAI API Base & Key**
```shell
$ export OPENAI_API_BASE=http://0.0.0.0:8000
```
**Step 3 Run with FLASK**
```shell
git clone https://github.com/kaistAI/FLASK
```
```shell
cd FLASK/gpt_review
```
Run the eval
```shell
python gpt4_eval.py -q '../evaluation_set/flask_evaluation.jsonl'
```
</TabItem>
<TabItem value="FastEval" label="Fast Eval">
**Step 1: Start the local proxy**
see supported models [here](https://docs.litellm.ai/docs/simple_proxy)
```shell
$ litellm --model huggingface/bigcode/starcoder
```
**Step 2: Set OpenAI API Base & Key**
```shell
$ export OPENAI_API_BASE=http://0.0.0.0:8000
```
Set this to anything since the proxy has the credentials
```shell
export OPENAI_API_KEY=anything
```
**Step 3 Run with FastEval**
**Clone FastEval**
```shell
# Clone this repository, make it the current working directory
git clone --depth 1 https://github.com/FastEval/FastEval.git
cd FastEval
```
**Set API Base on FastEval**
On FastEval make the following **2 line code change** to set `OPENAI_API_BASE`
https://github.com/FastEval/FastEval/pull/90/files
```python
try:
api_base = os.environ["OPENAI_API_BASE"] #changed: read api base from .env
if api_base == None:
api_base = "https://api.openai.com/v1"
response = await self.reply_two_attempts_with_different_max_new_tokens(
conversation=conversation,
api_base=api_base, # #changed: pass api_base
api_key=os.environ["OPENAI_API_KEY"],
temperature=temperature,
max_new_tokens=max_new_tokens,
```
**Run FastEval**
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`
Since LiteLLM provides an OpenAI compatible proxy `-t` and `-m` don't need to change
`-t` will remain openai
`-m` will remain gpt-3.5
```shell
./fasteval -b human-eval-plus -t openai -m gpt-3.5-turbo
```
</TabItem>
<TabItem value="mlflow" label="ML Flow Eval">
MLflow provides an API `mlflow.evaluate()` to help evaluate your LLMs https://mlflow.org/docs/latest/llms/llm-evaluate/index.html
#### Pre Requisites
```shell
pip install litellm
```
```shell
pip install mlflow
```
#### Step 1: Start LiteLLM Proxy on the CLI
LiteLLM allows you to create an OpenAI compatible server for all supported LLMs. [More information on litellm proxy here](https://docs.litellm.ai/docs/simple_proxy)
```shell
$ litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on http://0.0.0.0:8000
```
#### Step 2: Run ML Flow
Before running the eval we will set `openai.api_base` to the litellm proxy from Step 1
```python
openai.api_base = "http://0.0.0.0:8000"
```
```python ```python
import openai import openai
import pandas as pd client = openai.OpenAI(
openai.api_key = "anything" # this can be anything, we set the key on the proxy api_key="anything",
openai.api_base = "http://0.0.0.0:8000" # set api base to the proxy from step 1 base_url="http://0.0.0.0:8000"
import mlflow
eval_data = pd.DataFrame(
{
"inputs": [
"What is the largest country",
"What is the weather in sf?",
],
"ground_truth": [
"India is a large country",
"It's cold in SF today"
],
}
) )
with mlflow.start_run() as run: # request sent to model set on litellm proxy, `litellm --model`
system_prompt = "Answer the following question in two sentences" response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
logged_model_info = mlflow.openai.log_model( {
model="gpt-3.5", "role": "user",
task=openai.ChatCompletion, "content": "this is a test request, write a short poem"
artifact_path="model", }
messages=[ ])
{"role": "system", "content": system_prompt},
{"role": "user", "content": "{question}"},
],
)
# Use predefined question-answering metrics to evaluate our model.
results = mlflow.evaluate(
logged_model_info.model_uri,
eval_data,
targets="ground_truth",
model_type="question-answering",
)
print(f"See aggregated evaluation results below: \n{results.metrics}")
# Evaluation result for each data record is available in `results.tables`.
eval_table = results.tables["eval_results_table"]
print(f"See evaluation table below: \n{eval_table}")
print(response)
``` ```
</TabItem> </TabItem>