Instructions to use TIGER-Lab/Qwen2.5-32B-Instruct-CFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/Qwen2.5-32B-Instruct-CFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TIGER-Lab/Qwen2.5-32B-Instruct-CFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/Qwen2.5-32B-Instruct-CFT")
model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/Qwen2.5-32B-Instruct-CFT", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TIGER-Lab/Qwen2.5-32B-Instruct-CFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/Qwen2.5-32B-Instruct-CFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/Qwen2.5-32B-Instruct-CFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TIGER-Lab/Qwen2.5-32B-Instruct-CFT

SGLang

How to use TIGER-Lab/Qwen2.5-32B-Instruct-CFT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TIGER-Lab/Qwen2.5-32B-Instruct-CFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/Qwen2.5-32B-Instruct-CFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TIGER-Lab/Qwen2.5-32B-Instruct-CFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/Qwen2.5-32B-Instruct-CFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TIGER-Lab/Qwen2.5-32B-Instruct-CFT with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/Qwen2.5-32B-Instruct-CFT
```

Qwen2.5-32B-Instruct-CFT / README.md

ubowang

nielsr HF Staff

Add library name and pipeline tag (#1)

4fdc411 verified almost 2 years ago

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metadata

license: apache-2.0
datasets:
  - TIGER-Lab/WebInstruct-CFT
language:
  - en
base_model:
  - Qwen/Qwen2.5-32B-Instruct
tags:
  - cft
  - math
  - reasoning
pipeline_tag: text-generation
library_name: transformers

Qwen2.5-32B-Instruct-CFT

Introduction

Qwen2.5-32B-Instruct-CFT is a 32B parameter model fine-tuned using our novel Critique Fine-Tuning (CFT) approach. Built upon the Qwen2.5-32B-Instruct base model, this variant is trained to critique and analyze responses rather than simply imitate them, leading to enhanced reasoning capabilities.

Key Features

Built on the powerful Qwen2.5-32B-Instruct foundation
Trained using Critique Fine-Tuning (CFT) methodology
Highly efficient training with minimal data requirements
Inherits the strong instruction-following capabilities of the base model

Training Details

Training Data

Dataset: WebInstruct-CFT-4K
Training format: (input=[query; noisy response], output=critique)
Teacher model: GPT-4o for generating critiques

Training Infrastructure

Framework: LLaMA-Factory
Hardware: 8x NVIDIA H100 GPUs
Training time: ~1.5 hours with DeepSpeed Zero-3

For more details about the model architecture, methodology, and comprehensive evaluation results, please visit our project webpage.