Instructions to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF")
model = AutoModelForCausalLM.from_pretrained("CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF")

llama-cpp-python

How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF",
	filename="Qwen2.5-7B-R1-Zero-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

Use Docker

docker model run hf.co/CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

SGLang

How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF 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 "CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF" \
    --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": "CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF",
		"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 "CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF" \
        --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": "CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with Ollama:
```
ollama run hf.co/CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M
```

Unsloth Studio new

How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF to start chatting

Pi new

How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with Docker Model Runner:
```
docker model run hf.co/CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M
```

Lemonade

How to use CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-7B-R1-Zero-GGUF-Q4_K_M

List all available models

lemonade list

GGUFs of CaptainHPY/Qwen2.5-7B-R1-Zero.

Training procedure

Quickstart

Ollama:

Install ollama:

apt-get update
apt-get install pciutils -y
curl -fsSL https://ollama.com/install.sh | sh

Run the model:

ollama run hf.co/CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M

Llama.cpp:

Install llama.cpp:

apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build \
    -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-gguf-split
cp llama.cpp/build/bin/llama-* llama.cpp

Pull from HuggingFace:

./llama.cpp/llama-cli \
    -hf CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF:Q4_K_M \
    --jinja -ngl 99 --threads -1 --ctx-size 32684 \
    --temp 0.7 --min-p 0.0 --top-p 0.80 --top-k 20 --repeat-penalty 1.05

Or Download the model after pip install huggingface_hub hf_transfer:

import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id = "CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF",
    local_dir = "CaptainHPY/Qwen2.5-7B-R1-Zero-GGUF",
    allow_patterns = ["*Q4_K_M*"],
)

Citations

Cite GRPO as:

@article{zhihong2024deepseekmath,
    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:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}

Cite Qwen2.5 as:

@misc{qwen2.5,
    title = {Qwen2.5: A Party of Foundation Models},
    url = {https://qwenlm.github.io/blog/qwen2.5/},
    author = {Qwen Team},
    month = {September},
    year = {2024}
}
@article{qwen2,
      title={Qwen2 Technical Report}, 
      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
      journal={arXiv preprint arXiv:2407.10671},
      year={2024}
}