---
license: apache-2.0
base_model: unsloth/gemma-3-270m-it
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- monostate
datasets:
- custom
language:
- en
library_name: transformers
pipeline_tag: text-generation
---

# monostate-model-e1098a75

This model is a fine-tuned version of [unsloth/gemma-3-270m-it](https://huggingface.co/unsloth/gemma-3-270m-it).

## Model Description

This model was fine-tuned using the Monostate training platform with LoRA (Low-Rank Adaptation) for efficient training.

## Training Details

### Training Data
- Dataset size: 162 samples
- Training type: Supervised Fine-Tuning (SFT)

### Training Procedure

#### Training Hyperparameters
- Training regime: Mixed precision (fp16)
- Optimizer: AdamW
- LoRA rank: 128
- LoRA alpha: 128
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

#### Training Results
- Final loss: 1.1394791555404664
- Training time: 0.49 minutes
- Generated on: 2025-09-14T15:31:15.380015

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("andrewmonostate/monostate-model-e1098a75")
tokenizer = AutoTokenizer.from_pretrained("andrewmonostate/monostate-model-e1098a75")

# Generate text
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        do_sample=True,
        top_p=0.95,
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## Framework Versions

- Transformers: 4.40+
- PyTorch: 2.0+
- Datasets: 2.0+
- Tokenizers: 0.19+

## License

This model is licensed under the Apache 2.0 License.

## Citation

If you use this model, please cite:

```bibtex
@misc{andrewmonostate_monostate_model_e1098a75,
  title={monostate-model-e1098a75},
  author={Monostate},
  year={2024},
  publisher={HuggingFace},
  url={https://huggingface.co/andrewmonostate/monostate-model-e1098a75}
}
```

## Training Platform

This model was trained using [Monostate](https://monostate.ai), an AI training and deployment platform.