Instructions to use itsmepv/gpt2-ppo-full-parameter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use itsmepv/gpt2-ppo-full-parameter with Transformers:

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

pipe = pipeline("text-generation", model="itsmepv/gpt2-ppo-full-parameter")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("itsmepv/gpt2-ppo-full-parameter")
model = AutoModelForCausalLM.from_pretrained("itsmepv/gpt2-ppo-full-parameter")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use itsmepv/gpt2-ppo-full-parameter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "itsmepv/gpt2-ppo-full-parameter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itsmepv/gpt2-ppo-full-parameter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/itsmepv/gpt2-ppo-full-parameter

SGLang

How to use itsmepv/gpt2-ppo-full-parameter 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 "itsmepv/gpt2-ppo-full-parameter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itsmepv/gpt2-ppo-full-parameter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "itsmepv/gpt2-ppo-full-parameter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itsmepv/gpt2-ppo-full-parameter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use itsmepv/gpt2-ppo-full-parameter with Docker Model Runner:
```
docker model run hf.co/itsmepv/gpt2-ppo-full-parameter
```

gpt2-ppo-full-parameter / README.md

itsmepv

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metadata

base_model: gpt2-medium
library_name: transformers
model_name: gpt2-ppo-full-parameter
tags:
  - generated_from_trainer
  - trl
  - grpo
licence: license

Model Card for gpt2-ppo-full-parameter

This model is a fine-tuned version of gpt2-medium. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="itsmepv/gpt2-ppo-full-parameter", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Framework versions

TRL: 1.0.0.dev0
Transformers: 5.2.0
Pytorch: 2.9.0+cu126
Datasets: 4.0.0
Tokenizers: 0.22.2

Citations

Cite GRPO as:

@article{shao2024deepseekmath,
    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
    year         = 2024,
    eprint       = {arXiv:2402.03300},
}

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}