Environment:Huggingface Alignment handbook Evaluation Tools
| Knowledge Sources | |
|---|---|
| Domains | NLP, Evaluation |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
External evaluation environment with FastChat (MT-Bench) and AlpacaEval for benchmarking chat model quality using LLM-as-judge approaches.
Description
The alignment-handbook recommends two evaluation benchmarks for assessing fine-tuned chat models:
MT-Bench (from LMSYS/FastChat): A multi-turn benchmark spanning 80 dialogues across 10 domains, evaluated by GPT-4 as judge. It requires the FastChat library and generates model responses followed by GPT-4 rankings.
AlpacaEval: A single-turn benchmark evaluating helpfulness against text-davinci-003, using LLM-based automatic evaluation.
Both are external tools installed and run separately from the alignment-handbook training pipeline.
Usage
Use this environment after training to evaluate the quality of fine-tuned chat models. This is the final stage of any alignment pipeline, used to validate that the trained model meets quality benchmarks.
System Requirements
| Category | Requirement | Notes |
|---|---|---|
| Hardware | GPU for model inference | vLLM recommended for fast generation |
| Network | Internet access | For GPT-4 API calls (MT-Bench and AlpacaEval) |
Dependencies
External Tools
- FastChat (for MT-Bench) - installed from GitHub
- AlpacaEval - installed from GitHub
- vLLM (optional, for fast inference)
Credentials
- `OPENAI_API_KEY`: Required for GPT-4 judging in MT-Bench and AlpacaEval.
Quick Install
# MT-Bench (via FastChat)
pip install "fschat[model_worker,webui]"
# AlpacaEval
pip install alpaca-eval
# Optional: vLLM for fast inference
pip install vllm
Code Evidence
Evaluation instructions from `scripts/README.md:119-141`:
## Evaluating chat models
We recommend benchmarking chat models on:
* MT-Bench: a multi-turn benchmark spanning 80 dialogues and 10 domains.
* AlpacaEval: a single-turn benchmark that evaluates helpfulness of chat
and instruct models against text-davinci-003.
Chat template requirement for MT-Bench from `scripts/README.md:129`:
Make sure the word `zephyr` exists in the `--model-path` argument when
generating the model responses. This will ensure the correct chat template
is loaded.
Common Errors
| Error Message | Cause | Solution |
|---|---|---|
| `OpenAI API error: insufficient_quota` | OpenAI API quota exhausted | Check billing and quota on OpenAI dashboard |
| `Chat template not found for model` | Model path does not contain expected name | Include "zephyr" in the model path for MT-Bench compatibility |
Compatibility Notes
- LLM Judge Bias: Both MT-Bench and AlpacaEval use GPT-4 as judge, which exhibits preference for GPT-distilled models. The README recommends also submitting to Chatbot Arena for unbiased human evaluation.
- Chat Template: MT-Bench requires the model path to contain "zephyr" for the correct chat template to be loaded automatically.