Instructions to use salmannyu/step_cot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use salmannyu/step_cot with Transformers:

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

pipe = pipeline("text-generation", model="salmannyu/step_cot")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("salmannyu/step_cot")
model = AutoModelForMultimodalLM.from_pretrained("salmannyu/step_cot")
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 salmannyu/step_cot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "salmannyu/step_cot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "salmannyu/step_cot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/salmannyu/step_cot

SGLang

How to use salmannyu/step_cot 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 "salmannyu/step_cot" \
    --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": "salmannyu/step_cot",
		"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 "salmannyu/step_cot" \
        --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": "salmannyu/step_cot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use salmannyu/step_cot with Docker Model Runner:
```
docker model run hf.co/salmannyu/step_cot
```

step_cot / README.md

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metadata

library_name: transformers
license: other
base_model: Qwen/Qwen2.5-14B-Instruct
tags:
  - llama-factory
  - full
  - generated_from_trainer
model-index:
  - name: prm_cot
    results: []

prm_cot

This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct on the sky_math_step_level_gen_prm_cot dataset. It achieves the following results on the evaluation set:

Loss: 0.3308

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 4
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 32
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.3221	0.2539	500	0.3276
0.3095	0.5079	1000	0.3185
0.308	0.7618	1500	0.3138
0.2496	1.0157	2000	0.3163
0.248	1.2697	2500	0.3150
0.2526	1.5236	3000	0.3125
0.254	1.7776	3500	0.3094
0.1867	2.0315	4000	0.3303
0.1863	2.2854	4500	0.3317
0.1825	2.5394	5000	0.3310
0.1787	2.7933	5500	0.3310

Framework versions

Transformers 4.51.1
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1