Instructions to use NhatCuong22/qwen2.5-7b-proofdag-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NhatCuong22/qwen2.5-7b-proofdag-sft with Transformers:

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

pipe = pipeline("text-generation", model="NhatCuong22/qwen2.5-7b-proofdag-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NhatCuong22/qwen2.5-7b-proofdag-sft")
model = AutoModelForCausalLM.from_pretrained("NhatCuong22/qwen2.5-7b-proofdag-sft")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NhatCuong22/qwen2.5-7b-proofdag-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NhatCuong22/qwen2.5-7b-proofdag-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NhatCuong22/qwen2.5-7b-proofdag-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NhatCuong22/qwen2.5-7b-proofdag-sft

SGLang

How to use NhatCuong22/qwen2.5-7b-proofdag-sft 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 "NhatCuong22/qwen2.5-7b-proofdag-sft" \
    --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": "NhatCuong22/qwen2.5-7b-proofdag-sft",
		"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 "NhatCuong22/qwen2.5-7b-proofdag-sft" \
        --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": "NhatCuong22/qwen2.5-7b-proofdag-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NhatCuong22/qwen2.5-7b-proofdag-sft with Docker Model Runner:
```
docker model run hf.co/NhatCuong22/qwen2.5-7b-proofdag-sft
```

Qwen2.5-7B-Instruct — ProofDAG SFT

Full fine-tune của Qwen/Qwen2.5-7B-Instruct trên dataset ProofDAG (True / False / Uncertain).

Training


Data	5640 train / 330 val (multi-turn chat)
Hardware	8× L40 (FSDP FULL_SHARD, bf16)
Global batch	128, max_len 4096
LR	1e-6 cosine, warmup 0.03
Epochs	3 (132 steps, 6h 48m)
Final train / eval loss	0.207 / 0.251

Quick start

from transformers import AutoTokenizer, AutoModelForCausalLM

mid = "NhatCuong22/qwen2.5-7b-proofdag-sft"
tok = AutoTokenizer.from_pretrained(mid)
model = AutoModelForCausalLM.from_pretrained(mid, torch_dtype="bfloat16", device_map="auto")

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Premises:\n1. If it rains, the ground is wet.\n2. It rains.\n\nProposed conclusion: The ground is wet."},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=512)
print(tok.decode(out[0], skip_special_tokens=True))

License: Apache-2.0

Downloads last month: 82

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for NhatCuong22/qwen2.5-7b-proofdag-sft

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