Instructions to use cs-552-2026-the-transformers/math_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-552-2026-the-transformers/math_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cs-552-2026-the-transformers/math_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cs-552-2026-the-transformers/math_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-the-transformers/math_model") 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 cs-552-2026-the-transformers/math_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cs-552-2026-the-transformers/math_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cs-552-2026-the-transformers/math_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-the-transformers/math_model
- SGLang
How to use cs-552-2026-the-transformers/math_model 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 "cs-552-2026-the-transformers/math_model" \ --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": "cs-552-2026-the-transformers/math_model", "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 "cs-552-2026-the-transformers/math_model" \ --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": "cs-552-2026-the-transformers/math_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-the-transformers/math_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-the-transformers/math_model
| base_model: Qwen/Qwen3-1.7B | |
| library_name: transformers | |
| license: apache-2.0 | |
| pipeline_tag: text-generation | |
| tags: | |
| - math | |
| - text-generation | |
| # math_model | |
| A fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) | |
| for mathematical reasoning. The model produces a chain of reasoning and returns the | |
| final answer wrapped in `\boxed{...}`. | |
| ## Usage | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| tok = AutoTokenizer.from_pretrained("cs-552-2026-the-transformers/math_model") | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "cs-552-2026-the-transformers/math_model", device_map="cuda") | |
| msgs = [{"role": "user", "content": "What is 12 * 12? Put the final answer in \\boxed{}."}] | |
| ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda") | |
| out = model.generate(ids, max_new_tokens=2048) | |
| print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)) | |
| ``` | |