Instructions to use cs-552-2026-group1/math_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cs-552-2026-group1/math_model with Transformers:

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

pipe = pipeline("text-generation", model="cs-552-2026-group1/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-group1/math_model")
model = AutoModelForCausalLM.from_pretrained("cs-552-2026-group1/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-group1/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-group1/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-group1/math_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cs-552-2026-group1/math_model

SGLang

How to use cs-552-2026-group1/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-group1/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-group1/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-group1/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-group1/math_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cs-552-2026-group1/math_model with Docker Model Runner:
```
docker model run hf.co/cs-552-2026-group1/math_model
```

math_model / README.md

arnavbajpai

Update README.md

e00aab0 verified about 1 month ago

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	---
	license: mit
	language:
	- en
	base_model: Qwen/Qwen3-1.7B
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- qwen3
	- math
	- reasoning
	- olympiad-math
	- supervised-fine-tuning
	- lora
	- cs-552
	datasets:
	- EleutherAI/hendrycks_math
	- open-r1/OpenR1-Math-220k
	- AI-MO/NuminaMath-CoT
	metrics:
	- accuracy
	---

	# Math Model — Olympiad-Focused SFT Checkpoint

	This model is a fine-tuned version of Qwen/Qwen3-1.7B for mathematical reasoning, developed for the CS-552 standard project math track.

	The model was trained to solve competition-style mathematics problems and produce final answers in boxed LaTeX format.

	## Base Model

	- Base model: `Qwen/Qwen3-1.7B`
	- Fine-tuning method: LoRA supervised fine-tuning
	- Target task: mathematical reasoning and short-answer competition problems

	## Training Data

	The final submitted checkpoint was trained on approximately 25,165 examples from hard mathematical reasoning datasets:

	\| Dataset / Source \| Examples \|
	\|---\|---:\|
	\| Hendrycks MATH \| 4,759 \|
	\| OpenR1-Math-220k \| 7,999 \|
	\| NuminaMath-CoT \| 12,407 \|
	\| Total \| 25,165 \|

	The NuminaMath subset was filtered to focus on harder mathematical sources:

	- Olympiads
	- AoPS Forum
	- AMC/AIME
	- MATH

	The OpenR1 subset was filtered to competition-relevant categories:

	- Algebra
	- Geometry
	- Number Theory
	- Combinatorics
	- Inequalities

	## Training Details

	\| Setting \| Value \|
	\|---\|---\|
	\| Base model \| Qwen/Qwen3-1.7B \|
	\| Fine-tuning method \| LoRA \|
	\| Epochs \| 1 \|
	\| LoRA rank \| 32 \|
	\| LoRA alpha \| 64 \|
	\| LoRA dropout \| 0.05 \|
	\| Learning rate \| 1e-4 \|
	\| Batch size \| 1 \|
	\| Gradient accumulation steps \| 8 \|
	\| Precision \| bfloat16 \|
	\| Hardware \| 1 × NVIDIA A100 40GB \|
	\| Training steps \| 3,146 \|
	\| Runtime \| 6,761 seconds \|
	\| Tokens processed \| 14.7M \|
	\| Final training loss \| 0.6114 \|
	\| Mean token accuracy \| 0.8302 \|

	## Generation Configuration

	The submitted generation configuration uses sampling:

	```json
	{
	"do_sample": true,
	"temperature": 0.25,
	"top_p": 0.85,
	"top_k": 30
	}