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

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", 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=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
```

Automated MNLP evaluation report (2026-05-29)

by zechen-nlp - opened May 28

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+5459

-15

initial commitda27515a

Upload math SFT checkpoint (smoke test)2b172d75

Upload math SFT checkpoint (smoke test)ed23471f

Upload math SFT checkpointf13b0a9e

Automated MNLP evaluation report (2026-05-23) (#1)b8bbbde1

Upload exp4b: SFT→DPO v2 (L3-5, ≤5/8, beta=0.1, lr=5e-7)32ac537f

Automated MNLP evaluation report (2026-05-27) (#2)4bd50478

Fix tokenizer_config.json: is_local=false362bfeb3

Fix README: add base_model, fix model references6af8d3ad

Push DPO checkpoint with T=0.3 (optimal for pass@8 on CI gate)ac3517ea

zechen-nlp

May 28

Posted by the course CI pipeline.

Automated MNLP evaluation report (2026-05-28)3264a446

zechen-nlp changed pull request title from Automated MNLP evaluation report (2026-05-28) to Automated MNLP evaluation report (2026-05-29) May 29

Update Automated MNLP evaluation report (2026-05-29)b9930e55

jdecim changed pull request status to merged May 29

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