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

Libraries

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

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

pipe = pipeline("text-generation", model="willhx/Qwen3-8B-Base-Math")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("willhx/Qwen3-8B-Base-Math")
model = AutoModelForCausalLM.from_pretrained("willhx/Qwen3-8B-Base-Math")
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 Settings

vLLM

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

Use Docker

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

SGLang

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

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

Qwen3-8B-Base-Math / README.md

willhx

Upload folder using huggingface_hub

ce64d4b verified 7 days ago

preview code

Raw

History Blame Contribute Delete

2.94 kB

	---
	license: apache-2.0
	library_name: transformers
	---
	# Qwen3-8B-Base

	## Qwen3 Highlights

	Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.
	Building upon extensive advancements in training data, model architecture, and optimization techniques, Qwen3 delivers the following key improvements over the previously released Qwen2.5:

	- Expanded Higher-Quality Pre-training Corpus: Qwen3 is pre-trained on 36 trillion tokens across 119 languages — tripling the language coverage of Qwen2.5 — with a much richer mix of high-quality data, including coding, STEM, reasoning, book, multilingual, and synthetic data.
	- Training Techniques and Model Architecture: Qwen3 incorporates a series of training techiques and architectural refinements, including global-batch load balancing loss for MoE models and qk layernorm for all models, leading to improved stability and overall performance.
	- Three-stage Pre-training: Stage 1 focuses on broad language modeling and general knowledge acquisition, Stage 2 improves reasoning skills like STEM, coding, and logical reasoning, and Stage 3 enhances long-context comprehension by extending training sequence lengths up to 32k tokens.
	- Scaling Law Guided Hyperparameter Tuning: Through comprehensive scaling law studies across the three-stage pre-training pipeline, Qwen3 systematically tunes critical hyperparameters — such as learning rate scheduler and batch size — separately for dense and MoE models, resulting in better training dynamics and final performance across different model scales.

	## Model Overview

	Qwen3-8B-Base has the following features:
	- Type: Causal Language Models
	- Training Stage: Pretraining
	- Number of Parameters: 8.2B
	- Number of Paramaters (Non-Embedding): 6.95B
	- Number of Layers: 36
	- Number of Attention Heads (GQA): 32 for Q and 8 for KV
	- Context Length: 32,768

	For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).

	## Requirements

	The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.

	With `transformers<4.51.0`, you will encounter the following error:
	```
	KeyError: 'qwen3'
	```

	## Evaluation & Performance

	Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen3/).

	### Citation

	If you find our work helpful, feel free to give us a cite.

	```
	@misc{qwen3technicalreport,
	title={Qwen3 Technical Report},
	author={Qwen Team},
	year={2025},
	eprint={2505.09388},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2505.09388},
	}
	```