Instructions to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2 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-quant-GGUF-v2 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2: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 dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2: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 dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M

Use Docker

docker model run hf.co/dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M

LM Studio
Jan
Ollama
How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2 with Ollama:
```
ollama run hf.co/dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M
```

Unsloth Studio

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2 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-quant-GGUF-v2 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-quant-GGUF-v2 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-quant-GGUF-v2 to start chatting

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2: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": "dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2 with Docker Model Runner:
```
docker model run hf.co/dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M
```

Lemonade

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2: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 dumbequation/Qwen2.5-7B-GRPO-1M-Context-Medical-Reasoning-quant-GGUF-v2:Q4_K_M

Run Hermes

hermes

Atomic Chat

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: 80

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-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-quant-GGUF-v2

Base model

Qwen/Qwen2.5-7B

Finetuned

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

Quantized

(68)

this model

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Collection

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