---
license: gemma
base_model: google/gemma-3-1b-it
tags:
- fine-tuned
- gguf
- gemma
- instruct
- 1b
model_type: gemma3
quantized: f16
datasets:
- custom
language:
- en
pipeline_tag: text-generation
---

# Fine-tuned Gemma 3 1B IT (GGUF)

This is a fine-tuned version of Google's Gemma 3 1B IT model, converted to GGUF format for efficient inference.

## Model Details

- **Base Model**: google/gemma-3-1b-it
- **Fine-tuning Method**: QLoRA (Quantized Low-Rank Adaptation)
- **Format**: GGUF (F16 precision)
- **Size**: ~1.9GB

## Training Details

- **Adapter**: QLoRA with rank 32, alpha 16
- **Target Modules**: up_proj, down_proj, gate_proj, q_proj, k_proj, v_proj, o_proj
- **Sequence Length**: 2048
- **Training Data**: Custom dataset with sample packing
- **Epochs**: 3
- **Learning Rate**: 0.0004 with cosine scheduler
- **Optimizer**: AdamW BNB 8-bit

## Usage

This GGUF model can be used with various inference engines:

### llama.cpp
```bash
./llama-server -m model.gguf
```

### Ollama
```bash
# Create a Modelfile
FROM model.gguf
TEMPLATE """<start_of_turn>user
{{ .Prompt }}<end_of_turn>
<start_of_turn>model
"""

# Import the model
ollama create my-gemma-model -f Modelfile
ollama run my-gemma-model
```

### Python with llama-cpp-python
```python
from llama_cpp import Llama

llm = Llama(model_path="model.gguf")
output = llm("Tell me about artificial intelligence", max_tokens=512)
print(output)
```

## Chat Template

This model uses Gemma 3's chat template:
```
<start_of_turn>user
{user_message}<end_of_turn>
<start_of_turn>model
{assistant_response}<end_of_turn>
```

## Limitations

- This is a small 1B parameter model with inherent limitations
- Fine-tuned for specific use cases - performance may vary on other tasks
- GGUF conversion may introduce minor numerical differences compared to the original model

## License

This model inherits the license from the base Gemma model. Please refer to Google's Gemma license for usage terms.