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vLLM Evaluation and Grading Guide

This directory contains utilities for serving models via vLLM's OpenAI-compatible API server for fast evaluation and LLM-based grading.

Overview

The vLLM integration supports two workflows:

  1. Evaluation: Generate predictions using a model (typically your fine-tuned model)
  2. Grading: Judge predictions against expected outputs using an LLM (local vLLM server or OpenAI API)
┌─────────────────────┐
│  Evaluation Model   │  ← Generates code execution predictions
│  (vLLM or HF)       │
└─────────────────────┘
          ↓
    [Predictions]
          ↓
┌─────────────────────┐
│   Grading Model     │  ← Judges predictions vs. expected outputs
│  (vLLM or OpenAI)   │
└─────────────────────┘
          ↓
    [Scores: 0.0-1.0]

Prerequisites

  • Python >= 3.10
  • CUDA-capable GPU(s)
  • uv package manager (recommended)
  • .env file: The script requires a .env file at the repository root with environment variables (see .env.template for setup). The script automatically searches up the directory tree to find it.

Quick Setup

# Install uv if not already available
curl -LsSf https://astral.sh/uv/install.sh | sh

# Navigate to the vllm_eval directory
cd scripts/vllm_eval

# Dependencies will be automatically installed when running scripts via uv
uv run python vllm_server.py --help

Server Configuration

Model Selection

  • --model: HuggingFace model name (e.g., Qwen/Qwen3-4B-Instruct-2507)
  • --checkpoint_path: Path to local checkpoint directory
  • --base_model: Base model for LoRA merging (if not in adapter_config.json)
  • --merge_lora: Enable LoRA adapter merging (auto-enabled if adapter detected)

Server Options

  • --host: Bind address (default: 0.0.0.0 for network access)
  • --port: Server port (default: 8000)
  • --model_name: Name clients use in API calls. If not specified, auto-derived:
    • For HuggingFace models: uses full model string (e.g., "Qwen/Qwen3-4B-Instruct-2507")
    • For checkpoints: uses last 3 path components (e.g., "runs/exp_123/checkpoint-1600")
  • --max_model_len: Maximum sequence length (default: model's max)
  • --trust_remote_code: Allow custom model code (default: true, required for Qwen)

GPU Selection

  • --cuda_devices: Comma-separated GPU device IDs (e.g., 0,1,2,3)
  • --tensor_parallel_size: Number of GPUs for tensor parallelism (default: 8)
  • --gpu_memory_utilization: GPU memory fraction (default: 0.9)

Important for running multiple servers: Use --cuda_devices to assign non-overlapping GPUs when running evaluation and grading servers simultaneously.

LoRA-Specific Options

  • --merged_model_dir: Where to save merged model (default: <PYINE_CACHE_ROOT>/vllm_merged_models/<checkpoint_name> where checkpoint name is auto-derived from the last 3 path components)
  • --force_merge: Re-merge even if merged model exists

Usage: Evaluation Only

1. Serve a HuggingFace Model Directly

# Single GPU
python vllm_server.py --model Qwen/Qwen3-4B-Instruct-2507 --cuda_devices 0
# Model name will be: "Qwen/Qwen3-4B-Instruct-2507"

# Multi-GPU (4 GPUs)
python vllm_server.py \
    --model Qwen/Qwen3-4B-Instruct-2507 \
    --cuda_devices 0,1,2,3
# Model name will be: "Qwen/Qwen3-4B-Instruct-2507"

2. Serve a Local Checkpoint (Full Model)

python vllm_server.py \
    --checkpoint_path /path/to/checkpoint \
    --cuda_devices 0,1,2,3 \
    --port 8000
# Model name auto-derived from checkpoint path (last 3 components)
# Example: "/home/user/runs/exp_123/checkpoint-1600" → "runs/exp_123/checkpoint-1600"

3. Serve a LoRA Checkpoint (with Merging)

LoRA checkpoints store only adapter weights. The script automatically merges them with the base model.

# Auto-detected base model from adapter_config.json
python vllm_server.py \
    --checkpoint_path /path/to/lora_checkpoint \
    --merge_lora
# Model name auto-derived from checkpoint path (last 3 components)

# Explicitly specify base model
python vllm_server.py \
    --checkpoint_path /path/to/lora_checkpoint \
    --base_model Qwen/Qwen3-4B-Instruct-2507 \
    --merge_lora \
    --cuda_devices 0,1,2,3
# Model name auto-derived from checkpoint path (last 3 components)

# Override model name if needed
python vllm_server.py \
    --checkpoint_path /path/to/lora_checkpoint \
    --base_model Qwen/Qwen3-4B-Instruct-2507 \
    --merge_lora \
    --model_name my-custom-model

Note: Merging happens only once. The merged model is cached in <PYINE_CACHE_ROOT>/vllm_merged_models/<checkpoint_name> (using the checkpoint path naming convention) and reused on subsequent runs unless you specify --force_merge. This allows merged models to be stored in a centralized location and reused across different server launches.

4. Run Evaluation

Once your server is running, use the evaluation config:

# Run evaluation with vLLM inference (faster than HuggingFace)
uv run python -m pyine.apps.trainers.hf_trainer \
    +experiment=original/v0_50perc_dataset_qwen3_vllm_eval.yaml

Usage: Evaluation with Grading

Grading uses an LLM to judge if predictions match expected outputs. This provides more flexible matching than exact string comparison.

Setup Option 1: Two vLLM Servers

Terminal 1 - Evaluation Model (fine-tuned on GPUs 0-3):

python scripts/vllm_eval/vllm_server.py \
    --checkpoint_path /path/to/finetuned/checkpoint \
    --base_model Qwen/Qwen3-4B-Instruct-2507 \
    --port 8000 \
    --cuda_devices 0,1,2,3
# Model name auto-derived from checkpoint path (e.g., "finetuned/exp_123/checkpoint-1600")

Terminal 2 - Grading Model (powerful base model on GPUs 4-7):

python scripts/vllm_eval/vllm_server.py \
    --model Qwen/Qwen3-4B-Instruct-2507 \
    --port 8001 \
    --cuda_devices 4,5,6,7
# Model name will be: "Qwen/Qwen3-4B-Instruct-2507"

Notes:

  • The --cuda_devices argument prevents GPU overlap when running multiple servers
  • Model names are auto-derived but can be overridden with --model_name if needed
  • Make sure your config file uses the correct auto-derived model names in the model: fields

Run Evaluation with Grading:

uv run python -m pyine.apps.trainers.hf_trainer \
    +experiment=original/v0_50perc_dataset_qwen3_vllm_eval_vllm_grading.yaml

Testing Grading Independently

Before running full evaluation, it is possible to test grading as follows (after having launched the grading vLLM server as seen previously):

# Test vLLM grading (default: grading server on port 8001)
uv run python scripts/vllm_eval/test_vllm_grading.py

# Test with custom server URL and model name
uv run python scripts/vllm_eval/test_vllm_grading.py \
    --base_url http://localhost:8001/v1 \
    --model grader-model

Expected output:

Testing VLLM Grader
================================================================================
Grader available: True

--- Test Case 1: Exact match ---
Expected: '42'
Predicted: '42'

...

Metrics:
  accuracy_hard: 0.75
  accuracy_soft: 1.0
  accuracy_grader: 0.75

✓ vLLM grading test completed successfully

Example Configurations