Instructions to use willhx/Qwen3-8B-Base-Math-TauSFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use willhx/Qwen3-8B-Base-Math-TauSFT with Transformers:

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

pipe = pipeline("text-generation", model="willhx/Qwen3-8B-Base-Math-TauSFT")
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("willhx/Qwen3-8B-Base-Math-TauSFT")
model = AutoModelForCausalLM.from_pretrained("willhx/Qwen3-8B-Base-Math-TauSFT", 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 willhx/Qwen3-8B-Base-Math-TauSFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "willhx/Qwen3-8B-Base-Math-TauSFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "willhx/Qwen3-8B-Base-Math-TauSFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/willhx/Qwen3-8B-Base-Math-TauSFT

SGLang

How to use willhx/Qwen3-8B-Base-Math-TauSFT 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 "willhx/Qwen3-8B-Base-Math-TauSFT" \
    --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": "willhx/Qwen3-8B-Base-Math-TauSFT",
		"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 "willhx/Qwen3-8B-Base-Math-TauSFT" \
        --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": "willhx/Qwen3-8B-Base-Math-TauSFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use willhx/Qwen3-8B-Base-Math-TauSFT with Docker Model Runner:
```
docker model run hf.co/willhx/Qwen3-8B-Base-Math-TauSFT
```

Qwen3-8B-Base-Math-TauSFT / tokenizer.json

willhx

Upload final Math-TauSFT checkpoint (step 405) in BF16 Hugging Face format

ba8a435 verified 12 days ago

Raw History Contribute Delete

7.03 MB

File too large to display, you can check the raw version instead.