Instructions to use TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF 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 TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-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 TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-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 TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF with Ollama:
```
ollama run hf.co/TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M
```
Unsloth Desktop

How to use TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF with Docker Model Runner:
```
docker model run hf.co/TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M
```

Lemonade

How to use TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-3B-Instruct-grpo-gmail-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-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 TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF: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 "TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF: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"

Qwen2.5-3B-Instruct-grpo-gmail-GGUF / README.md

TurkishCodeMan

Upload README.md with huggingface_hub

1f6740a verified 10 months ago

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	---
	language:
	- en
	license: apache-2.0
	base_model: TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail
	quantized_by: TurkishCodeMan
	tags:
	- gguf
	- quantized
	- tool-calling
	- llama.cpp
	- 4-bit
	---

	# Qwen2.5-3B-Instruct GRPO Gmail (Q4_K_M GGUF)

	Quantized version of [TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail](https://huggingface.co/TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail).

	## 📥 Download & Run

	\`\`\`bash
	# Download (recommended - 3.5 GB)
	huggingface-cli download TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail-GGUF Qwen2.5-3B-Instruct-grpo-gmail-Q4_K_M.gguf

	# Run with GPU
	./llama-server -m Qwen2.5-3B-Instruct-grpo-gmail-Q4_K_M.gguf --port 8080 -ngl 99

	# Run on CPU
	./llama-server -m Qwen2.5-3B-Instruct-grpo-gmail-Q4_K_M.gguf --port 8080
	\`\`\`

	## ⚙️ Quantization Info

	- Method: Q4_K_M (4-bit with K-means)
	- Size: ~2.3 GB (vs 6.7 GB F16)
	- Quality: 95%+ of F16 performance
	- Speed: 3-4x faster inference

	## 🔗 Related Models

	- Full precision (F16): [TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail](https://huggingface.co/TurkishCodeMan/Qwen2.5-3B-Instruct-grpo-gmail)
	- Base model: [unsloth/Qwen2.5-3B-Instruct](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct)

	## 🎯 Tool Calling Example

	\`\`\`python
	import requests

	response = requests.post("http://localhost:8080/v1/chat/completions", json={
	"messages": [
	{"role": "system", "content": "You are a tool-calling assistant."},
	{"role": "user", "content": "Send email to test@gmail.com about meeting tomorrow"}
	],
	"temperature": 0.0,
	"max_tokens": 512
	})

	print(response.json()['choices'][0]['message']['content'])
	# Output: {"tool_calls": [{"function": "send_email", "arguments": {"to": ["test@gmail.com"], "subject": "Meeting Tomorrow", "body": "..."}}]}
	\`\`\`

	## 📊 Training

	- SFT: 300 steps on 57 Gmail examples
	- GRPO: 300 steps reinforcement learning for tool calling accuracy
	- Final loss: 0.50 (excellent convergence)

	## 🛠️ Supported Tools

	\`send_email\`, \`draft_email\`, \`read_email\`, \`search_emails\`, \`delete_email\`, \`modify_email\`, \`batch_modify_emails\`, \`batch_delete_emails\`, \`list_email_labels\`, \`create_label\`, \`update_label\`, \`delete_label\`, \`get_or_create_label\`, \`create_filter\`, \`list_filters\`, \`get_filter\`, \`delete_filter\`, \`create_filter_from_template\`, \`download_attachment\`