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")
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
```

math_model / split_info.json

jdecim

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

ac3517e verified 9 days ago

raw

history blame

860 Bytes

	{
	"experiment_name": "exp4b_dpo",
	"config_file": "/scratch/jennifer/standard-project-m2-the-transformers/configs/exp4b_dpo.yaml",
	"resolved_config": {
	"num_train_epochs": 1,
	"learning_rate": 5e-07,
	"per_device_train_batch_size": 1,
	"per_device_eval_batch_size": 1,
	"gradient_accumulation_steps": 8,
	"warmup_ratio": 0.1,
	"seed": 42,
	"save_total_limit": 3,
	"eval_steps": 50,
	"logging_steps": 10,
	"beta": 0.1,
	"loss_type": "sigmoid",
	"max_length": 4096,
	"max_completion_length": 3072,
	"val_fraction": 0.1,
	"wandb_project": "cs552-math-dpo",
	"experiment_name": "exp4b_dpo",
	"output_dir": "/scratch/checkpoints/exp4b_dpo",
	"sft_checkpoint": "/scratch/checkpoints/sft_mixlong_full",
	"train_source": "/scratch/data/dpo_pairs_v2/pairs.jsonl"
	},
	"n_train": 5048,
	"n_val": 561
	}