Instructions to use HaimingW/qwen2.5-1.5b-faithful-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HaimingW/qwen2.5-1.5b-faithful-summarization with Transformers:

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

pipe = pipeline("text-generation", model="HaimingW/qwen2.5-1.5b-faithful-summarization")
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("HaimingW/qwen2.5-1.5b-faithful-summarization")
model = AutoModelForCausalLM.from_pretrained("HaimingW/qwen2.5-1.5b-faithful-summarization", 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 HaimingW/qwen2.5-1.5b-faithful-summarization with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HaimingW/qwen2.5-1.5b-faithful-summarization"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HaimingW/qwen2.5-1.5b-faithful-summarization",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HaimingW/qwen2.5-1.5b-faithful-summarization

SGLang

How to use HaimingW/qwen2.5-1.5b-faithful-summarization 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 "HaimingW/qwen2.5-1.5b-faithful-summarization" \
    --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": "HaimingW/qwen2.5-1.5b-faithful-summarization",
		"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 "HaimingW/qwen2.5-1.5b-faithful-summarization" \
        --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": "HaimingW/qwen2.5-1.5b-faithful-summarization",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HaimingW/qwen2.5-1.5b-faithful-summarization with Docker Model Runner:
```
docker model run hf.co/HaimingW/qwen2.5-1.5b-faithful-summarization
```

Qwen2.5-1.5B Faithful Summarization SFT

This model is a fine-tuned version of Qwen/Qwen2.5-1.5B for faithful document summarization.

Model Details

Developed by: HaimingW
Model type: Causal Language Model (Fine-tuned)
Language(s): English
License: Same as base model (Qwen License)
Finetuned from model: Qwen/Qwen2.5-1.5B

Training Details

Training Data

Decontaminated public summarization datasets:
- XSum
- CNN/DailyMail
- BillSum
Decontaminated against held-out evaluation set using exact substring + n-gram overlap filtering
35,659 training examples after filtering

Training Hyperparameters

Method: LoRA SFT (r=32, alpha=64, all linear layers) with merged full-weight checkpoint
Epochs: 1
Learning rate: 2e-4 with cosine decay
Batch size: 1 per device, gradient accumulation 16 (effective batch 32)
Max sequence length: 2048
Precision: bf16
Hardware: 2x NVIDIA H20

Evaluation

Evaluated on 360 held-out summarization items:

Faithfulness: 0.547
Coverage: 0.422
Combined score: 0.476 (target: 0.45)
Degenerate fraction: 11.9%

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("HaimingW/qwen2.5-1.5b-faithful-summarization", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("HaimingW/qwen2.5-1.5b-faithful-summarization")

messages = [
    {"role": "system", "content": "You are a helpful assistant that summarizes documents faithfully."},
    {"role": "user", "content": "Summarize the following document:\n\n<document text here>"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
summary = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

Citation

Please cite the base model and datasets:

Qwen2.5: Qwen team
XSum, CNN/DailyMail, BillSum datasets

Downloads last month: 20

Safetensors

Model size

2B params

Tensor type

F32

Model tree for HaimingW/qwen2.5-1.5b-faithful-summarization

Base model

Qwen/Qwen2.5-1.5B

Finetuned

(416)

this model