Instructions to use prithivMLmods/OpenR1-Distill-7B-F32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/OpenR1-Distill-7B-F32-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/OpenR1-Distill-7B-F32-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/OpenR1-Distill-7B-F32-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/OpenR1-Distill-7B-F32-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/OpenR1-Distill-7B-F32-GGUF",
	filename="OpenR1-Distill-7B.BF16.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 prithivMLmods/OpenR1-Distill-7B-F32-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/OpenR1-Distill-7B-F32-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 prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/OpenR1-Distill-7B-F32-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 prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/OpenR1-Distill-7B-F32-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 prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/OpenR1-Distill-7B-F32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/OpenR1-Distill-7B-F32-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": "prithivMLmods/OpenR1-Distill-7B-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/OpenR1-Distill-7B-F32-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 "prithivMLmods/OpenR1-Distill-7B-F32-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": "prithivMLmods/OpenR1-Distill-7B-F32-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 "prithivMLmods/OpenR1-Distill-7B-F32-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": "prithivMLmods/OpenR1-Distill-7B-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/OpenR1-Distill-7B-F32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/OpenR1-Distill-7B-F32-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 prithivMLmods/OpenR1-Distill-7B-F32-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 prithivMLmods/OpenR1-Distill-7B-F32-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/OpenR1-Distill-7B-F32-GGUF to start chatting

Pi new

How to use prithivMLmods/OpenR1-Distill-7B-F32-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/OpenR1-Distill-7B-F32-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": "prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/OpenR1-Distill-7B-F32-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 prithivMLmods/OpenR1-Distill-7B-F32-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 prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/OpenR1-Distill-7B-F32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/OpenR1-Distill-7B-F32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/OpenR1-Distill-7B-F32-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OpenR1-Distill-7B-F32-GGUF-Q4_K_M

List all available models

lemonade list

prithivMLmods commited on Jun 1, 2025

Commit

2e15f00

verified ·

1 Parent(s): f747fd3

Update README.md

Browse files

Files changed (1) hide show

README.md +27 -1

README.md CHANGED Viewed

@@ -10,4 +10,30 @@ pipeline_tag: text-generation
 library_name: transformers
 tags:
 - text-generation-inference
----

 library_name: transformers
 tags:
 - text-generation-inference
+---
+# **OpenR1-Distill-7B-F32-GGUF**
+> OpenR1-Distill-7B-F32-GGUF is a quantized version of OpenR1-Distill-7B, which is a post-trained model based on Qwen/Qwen2.5-Math-7B. It was further trained on Mixture-of-Thoughts, a curated dataset of 350k verified reasoning traces distilled from DeepSeek-R1. The dataset covers tasks in mathematics, coding, and science, and is designed to teach language models to reason step-by-step.
+## Model File
+| File Name                                | Size    | Format | Notes                            |
+|------------------------------------------|---------|--------|----------------------------------|
+| OpenR1-Distill-7B.BF16.gguf              | 15.2 GB | GGUF   | BF16 precision model             |
+| OpenR1-Distill-7B.F16.gguf               | 15.2 GB | GGUF   | FP16 precision model             |
+| OpenR1-Distill-7B.F32.gguf               | 30.5 GB | GGUF   | FP32 precision model             |
+| OpenR1-Distill-7B.Q2_K.gguf              | 3.02 GB | GGUF   | 2-bit quantized (Q2_K) model     |
+| OpenR1-Distill-7B.Q4_K_M.gguf            | 4.68 GB | GGUF   | 4-bit quantized (Q4_K_M) model   |
+| .gitattributes                           | 1.84 kB | Text   | Git LFS tracking config          |
+| config.json                              | 31 B    | JSON   | Model configuration file         |
+| README.md                                | 213 B   | Markdown | This readme file                |
+## Quants Usage
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)