---
license: gemma
language:
- en
- zh
base_model: twinkle-ai/gemma-3-4B-T1-it
library_name: transformers
tags:
- Taiwan
- SLM
- GGUF
- agent
datasets:
- lianghsun/tw-reasoning-instruct
- lianghsun/tw-contract-review-chat
- minyichen/tw-instruct-R1-200k
- minyichen/tw_mm_R1
- minyichen/LongPaper_multitask_zh_tw_R1
- nvidia/Nemotron-Instruction-Following-Chat-v1
metrics:
- accuracy
model-index:
- name: gemma-3-4B-T1-it
  results:
  - task:
      type: question-answering
      name: Single Choice Question
    dataset:
      name: tmmlu+
      type: ikala/tmmluplus
      config: all
      split: test
      revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c
    metrics:
    - type: accuracy
      value: 47.44
      name: single choice
  - task:
      type: question-answering
      name: Single Choice Question
    dataset:
      name: mmlu
      type: cais/mmlu
      config: all
      split: test
      revision: c30699e
    metrics:
    - type: accuracy
      value: 59.13
      name: single choice
  - task:
      type: question-answering
      name: Single Choice Question
    dataset:
      name: tw-legal-benchmark-v1
      type: lianghsun/tw-legal-benchmark-v1
      config: all
      split: test
      revision: 66c3a5f
    metrics:
    - type: accuracy
      value: 44.18
      name: single choice
pipeline_tag: text-generation
---

# Gemma 3 4B T1-it GGUF Collection

<div align="center" style="line-height: 1;">
  <a href="https://discord.gg/Cx737yw4ed" target="_blank" style="margin: 2px;">
    <img alt="Discord" src="https://img.shields.io/badge/Discord-Twinkle%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://huggingface.co/twinkle-ai" target="_blank" style="margin: 2px;">
    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Twinkle%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <!-- Gemma 模型在 Hugging Face 上為 gated，使用者需同意 Google usage license -->
  <a href="https://huggingface.co/google/gemma-3-4b-pt" style="margin: 2px;">
    <img alt="License" src="https://img.shields.io/badge/License-gemma-f5de53?&color=0081fb" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>

GGUF quantized models converted from [twinkle-ai/gemma-3-4B-T1-it](https://huggingface.co/twinkle-ai/gemma-3-4B-T1-it) for use with llama.cpp.

![Gemma3-4B-T1-it](https://cdn-uploads.huggingface.co/production/uploads/62b085e6a14cbd643867d561/wqX8cTImAyfmdLxrrGW4L.png)

## About

Gemma 3 4B T1-it is a small language model fine-tuned on Taiwan-focused datasets, supporting both English and Traditional Chinese. This repository provides multiple quantization formats optimized for different use cases.

## Available Models

| Model | Size | Use Case |
| ----- | ---- | -------- |
| `twinkle-ai-gemma-3-4B-T1-it-BF16.gguf` | Largest | Best quality, highest precision |
| `twinkle-ai-gemma-3-4B-T1-it-F16.gguf` | Large | High quality, good precision |
| `twinkle-ai-gemma-3-4B-T1-it-Q8_0.gguf` | Medium | Balanced quality and speed |
| `twinkle-ai-gemma-3-4b-t1-it-q4_k_m.gguf` | Smallest | Fastest inference, lower memory |

## Quick Start

### Option 1: Using Hugging Face Hub (Recommended)

Install llama.cpp via Homebrew:

```bash
brew install llama.cpp
```

Run inference directly from Hugging Face:

```bash
llama-cli --hf-repo thliang01/gemma-3-4B-T1-it-Q8_0-GGUF \
  --hf-file gemma-3-4b-t1-it-q8_0.gguf \
  -p "Your prompt here"
```

Start as a server:

```bash
llama-server --hf-repo thliang01/gemma-3-4B-T1-it-Q8_0-GGUF \
  --hf-file gemma-3-4b-t1-it-q8_0.gguf \
  -c 2048
```

### Option 2: Build from Source

#### Step 1: Clone llama.cpp repository

```bash
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
```

#### Step 2: Build llama.cpp

Basic build (CPU only):

```bash
LLAMA_CURL=1 make
```

**Hardware-specific build options:**

- **NVIDIA GPU (Linux)**:
  ```bash
  LLAMA_CUDA=1 LLAMA_CURL=1 make
  ```

- **Apple Silicon (Mac)**:
  ```bash
  LLAMA_METAL=1 LLAMA_CURL=1 make
  ```

- **AMD GPU (ROCm)**:
  ```bash
  LLAMA_HIPBLAS=1 LLAMA_CURL=1 make
  ```

#### Step 3: Run inference

```bash
./llama-cli --hf-repo thliang01/gemma-3-4B-T1-it-Q8_0-GGUF \
  --hf-file gemma-3-4b-t1-it-q8_0.gguf \
  -p "Your prompt here"
```

#### Step 4: Start server (optional)

```bash
./llama-server --hf-repo thliang01/gemma-3-4B-T1-it-Q8_0-GGUF \
  --hf-file gemma-3-4b-t1-it-q8_0.gguf \
  -c 2048
```

## Advanced Usage

### Choosing the Right Model

Select a model based on your needs:

- **Best Quality**: Use `BF16` or `F16` versions (requires more memory)
- **Balanced**: Use `Q8_0` version (recommended for most users)
- **Resource Constrained**: Use `q4_k_m` version (suitable for devices with limited memory)

### Common Parameters

- `-p "prompt"`: Your input text for the model to respond to
- `-c 2048`: Context length (maximum number of tokens that can be processed)
- `--hf-repo`: Hugging Face repository name
- `--hf-file`: Model file name to use

### Adjusting Generation Parameters

```bash
llama-cli --hf-repo thliang01/gemma-3-4B-T1-it-Q8_0-GGUF \
  --hf-file gemma-3-4b-t1-it-q8_0.gguf \
  -p "Your prompt here" \
  --temp 0.7 \
  --top-p 0.9 \
  --repeat-penalty 1.1
```

Parameter explanations:

- `--temp`: Temperature (0.0-2.0), higher values produce more random output
- `--top-p`: Nucleus sampling parameter (0.0-1.0)
- `--repeat-penalty`: Repetition penalty to avoid repetitive content

## Model Information

- **Base Model**: twinkle-ai/gemma-3-4B-T1-it
- **Languages**: English, Traditional Chinese
- **License**: Gemma
- **Format**: GGUF (converted via [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo))

### Training Data

- Taiwan reasoning and instruction datasets
- Contract review and legal documents
- Multimodal and long-form content
- Instruction-following examples

### Benchmarks

- **TMMLU+**: 47.44% accuracy
- **MMLU**: 59.13% accuracy
- **TW Legal Benchmark**: 44.18% accuracy

## Troubleshooting

### Common Issues

**Q: Getting out of memory errors?**

A: Try using a smaller quantized version like `q4_k_m`, or reduce the context length parameter `-c`.

**Q: How can I speed up inference?**

A:

1. Use GPU acceleration (add hardware-specific flags during compilation)
2. Choose a smaller quantized model (like `q4_k_m`)
3. Reduce context length

**Q: What prompt format does the model support?**

A: This is an instruction-tuned model. Use a clear instruction format, for example:

```text
Please analyze the main clauses of the following contract: [contract content]
```

## Links

- [Original Model](https://huggingface.co/twinkle-ai/gemma-3-4B-T1-it)
- [llama.cpp Documentation](https://github.com/ggerganov/llama.cpp)
- [GGUF Format Documentation](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md)

## Contributing

If you have any questions or suggestions, please feel free to open a discussion in the Hugging Face repository.

---

**Note**: On first run, llama.cpp will automatically download the model file from Hugging Face. Please ensure you have a stable internet connection.