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TurkishCodeMan
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Qwen2.5-3B-Instruct-grpo-gmail-GGUF

GGUF
English
quantized
tool-calling
llama.cpp
4-bit precision
conversational
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Qwen2.5-3B-Instruct-grpo-gmail-GGUF
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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\`