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e8e53a1

---
base_model: google/txgemma-9b-chat
language:
- en
library_name: transformers
license: other
license_name: health-ai-developer-foundations
license_link: https://developers.google.com/health-ai-developer-foundations/terms
pipeline_tag: text-generation
tags:
- therapeutics
- drug-development
- llama-cpp
- matrixportal
extra_gated_heading: Access TxGemma on Hugging Face
extra_gated_prompt: To access TxGemma on Hugging Face, you're required to review and
  agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
  To do this, please ensure you're logged in to Hugging Face and click below. Requests
  are processed immediately.
extra_gated_button_content: Acknowledge license
---

# matrixportal/txgemma-9b-chat-GGUF
   This model was converted to GGUF format from [`google/txgemma-9b-chat`](https://huggingface.co/google/txgemma-9b-chat) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
Refer to the [original model card](https://huggingface.co/google/txgemma-9b-chat) for more details on the model.

## ✅ Quantized Models Download List

### 🔍 Recommended Quantizations
- **✨ General CPU Use:** [`Q4_K_M`](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_k_m.gguf) (Best balance of speed/quality)
- **📱 ARM Devices:** [`Q4_0`](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_0.gguf) (Optimized for ARM CPUs)
- **🏆 Maximum Quality:** [`Q8_0`](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q8_0.gguf) (Near-original quality)

### 📦 Full Quantization Options
| 🚀 Download | 🔢 Type | 📝 Notes |
|:---------|:-----|:------|
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q2_k.gguf) | ![Q2_K](https://img.shields.io/badge/Q2_K-1A73E8) | Basic quantization |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q3_k_s.gguf) | ![Q3_K_S](https://img.shields.io/badge/Q3_K_S-34A853) | Small size |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q3_k_m.gguf) | ![Q3_K_M](https://img.shields.io/badge/Q3_K_M-FBBC05) | Balanced quality |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q3_k_l.gguf) | ![Q3_K_L](https://img.shields.io/badge/Q3_K_L-4285F4) | Better quality |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_0.gguf) | ![Q4_0](https://img.shields.io/badge/Q4_0-EA4335) | Fast on ARM |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_k_s.gguf) | ![Q4_K_S](https://img.shields.io/badge/Q4_K_S-673AB7) | Fast, recommended |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_k_m.gguf) | ![Q4_K_M](https://img.shields.io/badge/Q4_K_M-673AB7) ⭐ | Best balance |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q5_0.gguf) | ![Q5_0](https://img.shields.io/badge/Q5_0-FF6D01) | Good quality |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q5_k_s.gguf) | ![Q5_K_S](https://img.shields.io/badge/Q5_K_S-0F9D58) | Balanced |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q5_k_m.gguf) | ![Q5_K_M](https://img.shields.io/badge/Q5_K_M-0F9D58) | High quality |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q6_k.gguf) | ![Q6_K](https://img.shields.io/badge/Q6_K-4285F4) 🏆 | Very good quality |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q8_0.gguf) | ![Q8_0](https://img.shields.io/badge/Q8_0-EA4335) ⚡ | Fast, best quality |
| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-f16.gguf) | ![F16](https://img.shields.io/badge/F16-000000) | Maximum accuracy |

💡 **Tip:** Use `F16` for maximum precision when quality is critical

# GGUF Model Quantization & Usage Guide with llama.cpp

## What is GGUF and Quantization?

**GGUF** (GPT-Generated Unified Format) is an efficient model file format developed by the `llama.cpp` team that:
- Supports multiple quantization levels
- Works cross-platform
- Enables fast loading and inference

**Quantization** converts model weights to lower precision data types (e.g., 4-bit integers instead of 32-bit floats) to:
- Reduce model size
- Decrease memory usage
- Speed up inference
- (With minor accuracy trade-offs)

## Step-by-Step Guide

### 1. Prerequisites

```bash
# System updates
sudo apt update && sudo apt upgrade -y

# Dependencies
sudo apt install -y build-essential cmake python3-pip

# Clone and build llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make -j4
```

### 2. Using Quantized Models from Hugging Face

My automated quantization script produces models in this format:
```
https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_k_m.gguf
```

Download your quantized model directly:

```bash
wget https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_k_m.gguf
```

### 3. Running the Quantized Model

Basic usage:
```bash
./main -m txgemma-9b-chat-q4_k_m.gguf -p "Your prompt here" -n 128
```

Example with a creative writing prompt:
```bash
./main -m txgemma-9b-chat-q4_k_m.gguf        -p "[INST] Write a short poem about AI quantization in the style of Shakespeare [/INST]"        -n 256 -c 2048 -t 8 --temp 0.7
```

Advanced parameters:
```bash
./main -m txgemma-9b-chat-q4_k_m.gguf        -p "Question: What is the GGUF format?
Answer:"        -n 256 -c 2048 -t 8 --temp 0.7 --top-k 40 --top-p 0.9
```

### 4. Python Integration

Install the Python package:
```bash
pip install llama-cpp-python
```

Example script:
```python
from llama_cpp import Llama

# Initialize the model
llm = Llama(
    model_path="txgemma-9b-chat-q4_k_m.gguf",
    n_ctx=2048,
    n_threads=8
)

# Run inference
response = llm(
    "[INST] Explain GGUF quantization to a beginner [/INST]",
    max_tokens=256,
    temperature=0.7,
    top_p=0.9
)

print(response["choices"][0]["text"])
```

## Performance Tips

1. **Hardware Utilization**:
   - Set thread count with `-t` (typically CPU core count)
   - Compile with CUDA/OpenCL for GPU support

2. **Memory Optimization**:
   - Lower quantization (like q4_k_m) uses less RAM
   - Adjust context size with `-c` parameter

3. **Speed/Accuracy Balance**:
   - Higher bit quantization is slower but more accurate
   - Reduce randomness with `--temp 0` for consistent results

## FAQ

**Q: What quantization levels are available?**  
A: Common options include q4_0, q4_k_m, q5_0, q5_k_m, q8_0

**Q: How much performance loss occurs with q4_k_m?**  
A: Typically 2-5% accuracy reduction but 4x smaller size

**Q: How to enable GPU support?**  
A: Build with `make LLAMA_CUBLAS=1` for NVIDIA GPUs

## Useful Resources

1. [llama.cpp GitHub](https://github.com/ggerganov/llama.cpp)
2. [GGUF Format Specs](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md)
3. [Hugging Face Model Hub](https://huggingface.co/models)