Instructions to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF", dtype="auto")

llama-cpp-python

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF",
	filename="unsloth.F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16

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 dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16

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 dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16

Use Docker

docker model run hf.co/dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16

LM Studio
Jan
Ollama
How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF with Ollama:
```
ollama run hf.co/dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16
```

Unsloth Studio new

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-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 dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-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 dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF to start chatting

Pi new

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16

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": "Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF with Docker Model Runner:
```
docker model run hf.co/dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16
```

Lemonade

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF:F16

Run and chat with the model

lemonade run user.Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF-F16

List all available models

lemonade list

Qwen2.5 7B trained to think and reason like Deepseek R1, specifically on Diagnostic Medicine.

Use this to aid your differential diagnosis or ask questions or even just test it's reasoning.

Use the system prompt below for better results

Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>

Uploaded model

Developed by: dumbequation
License: apache-2.0
Finetuned from model : Qwen/Qwen2.5-7B-Instruct-1M

Downloads last month: 34

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct-1M

Quantized

(67)

this model

Collection including dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-f16-GGUF

Reasoning Work

Collection

Models I've trained to think like DeepSeek R1 using online learning - Group Relative Policy Optimization (GRPO) introduced by DeepSeekMath • 6 items • Updated May 1, 2025