gemma-3-270m-lora-finetuned/README.md

---
base_model: unsloth/gemma-3-270m-it
library_name: transformers
pipeline_tag: text-generation
license: gemma
tags:
- gemma3
- lora
- sft
- unsloth
- gguf
---

# gemma-3-270m-lora-finetuned

A fine-tuned Gemma 3 270M model with LoRA adapters, trained on instruction-following data.

## Model Details

- **Base Model:** unsloth/gemma-3-270m-it
- **Model Type:** LoRA fine-tuned (merged)
- **Language:** English (instruction-tuned)
- **License:** Gemma License

## Available Formats

| Format | File | Use Case |
|--------|------|----------|
| Safetensors | `model.safetensors` | Transformers, vLLM |
| GGUF Q8_0 | `gguf/gemma-3-270m-Q8_0.gguf` | llama.cpp, Ollama |
| GGUF BF16 | `gguf/gemma-3-270m-auto.gguf` | llama.cpp (full precision) |

## Uses

### Direct Use
- Instruction following
- Conversational AI
- Question answering
- Text generation

### Out-of-Scope Use
- Medical or legal advice
- Generating harmful content
- Production use without evaluation

## Training Details

### Training Data
- **Dataset:** mlabonne/FineTome-100k (full dataset)
- **Samples:** ~100,000
- **Data Processing:** Standardized format with Gemma 3 chat template

### Training Hyperparameters

| Parameter | Value |
|-----------|-------|
| Precision | bfloat16 |
| Max Sequence Length | 1024 |
| Per Device Batch Size | 2 |
| Gradient Accumulation Steps | 4 |
| Effective Batch Size | 8 |
| Max Steps | 12,500 |
| Learning Rate | 2e-4 |
| Warmup Steps | 2 |
| Optimizer | adamw_8bit |
| Weight Decay | 0.001 |
| LR Scheduler | linear |
| Seed | 3407 |

### LoRA Configuration

| Parameter | Value |
|-----------|-------|
| LoRA Rank (r) | 128 |
| LoRA Alpha | 128 |
| LoRA Dropout | 0 |
| Bias | none |
| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |

### Training Stats
- **Training Time:** ~98 minutes (1h 38m)
- **Peak VRAM Usage:** 12.4 GB (77.9% of 16 GB)

### Compute Infrastructure
- **Hardware:** NVIDIA RTX 5070 Ti (16 GB VRAM)
- **Framework:** Unsloth, Transformers, TRL

## How to Get Started

### Using Transformers (Safetensors)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("chanifrusydi/gemma-3-270m-lora-finetuned")
tokenizer = AutoTokenizer.from_pretrained("chanifrusydi/gemma-3-270m-lora-finetuned")

messages = [{"role": "user", "content": "Hello, how are you?"}]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
)

output = model.generate(inputs, max_new_tokens=64, temperature=0.7)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

### Using GGUF (llama.cpp / Ollama)
```bash
# Download GGUF
wget https://huggingface.co/chanifrusydi/gemma-3-270m-lora-finetuned/resolve/main/gguf/gemma-3-270m-Q8_0.gguf

# Run with llama.cpp
./llama-cli -m gemma-3-270m-Q8_0.gguf -p "Hello, how are you?"

# Or import to Ollama
ollama create gemma3-finetuned -f Modelfile
```

## Citation

```bibtex
@misc{gemma-3-270m-lora-finetuned,
  author = {chanifrusydi},
  title = {gemma-3-270m-lora-finetuned},
  publisher = {HuggingFace},
  year = {2025}
}
```

## Acknowledgments

- [Unsloth](https://github.com/unslothai/unsloth) for the fast fine-tuning framework
- [Google](https://huggingface.co/google) for the Gemma 3 base model
- [mlabonne](https://huggingface.co/mlabonne) for the FineTome-100k dataset