Instructions to use cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview with Transformers:

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

pipe = pipeline("text-generation", model="cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview")
model = AutoModelForCausalLM.from_pretrained("cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview

SGLang

How to use cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview 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 "cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview" \
    --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": "cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview",
		"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 "cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview" \
        --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": "cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview with Docker Model Runner:
```
docker model run hf.co/cutelemonlili/Qwen2.5-Math-7B-Instruct_MATH_training_Qwen_QwQ_32B_Preview
```

MATH_training_Qwen_QwQ_32B_Preview

This model is a fine-tuned version of Qwen/Qwen2.5-Math-7B-Instruct on the MATH_training_Qwen_QwQ_32B_Preview dataset. It achieves the following results on the evaluation set:

Loss: 0.2995

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 8
total_eval_batch_size: 4
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 2