---
base_model: unsloth/Qwen2.5-3B-Instruct-bnb-4bit
library_name: peft
pipeline_tag: text-generation
language: en
license: apache-2.0
tags:
- lora
- sft
- transformers
- trl
- unsloth
- fine-tuned
datasets:
- AiCloser/sharegpt_cot_dataset
---

# RRT1-3B

A fine-tuned 3B parameter model specialized for reasoning and chain-of-thought tasks

## Model Details

This model is a fine-tuned version of unsloth/Qwen2.5-3B-Instruct-bnb-4bit using the Unsloth framework with LoRA (Low-Rank Adaptation) for efficient training.

- **Developed by:** theprint
- **Model type:** Causal Language Model (Fine-tuned with LoRA)
- **Language:** en
- **License:** apache-2.0
- **Base model:** unsloth/Qwen2.5-3B-Instruct-bnb-4bit
- **Fine-tuning method:** LoRA with rank 128

## Intended Use

Reasoning, chain-of-thought, and general instruction following

## Training Details

### Training Data

ShareGPT conversations with chain-of-thought reasoning examples

- **Dataset:** AiCloser/sharegpt_cot_dataset
- **Format:** sharegpt

### Training Procedure

- **Training epochs:** 3
- **LoRA rank:** 128
- **Learning rate:** 0.0002
- **Batch size:** 4
- **Framework:** Unsloth + transformers + PEFT
- **Hardware:** NVIDIA RTX 5090

## Usage

```python
from unsloth import FastLanguageModel
import torch

# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="theprint/RRT1-3B",
    max_seq_length=4096,
    dtype=None,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

# Example usage
inputs = tokenizer(["Your prompt here"], return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## GGUF Quantized Versions

Quantized GGUF versions are available in the `gguf/` directory for use with llama.cpp:

- `RRT1-3B-q4_k_m.gguf` - 4-bit quantization (recommended for most use cases)
- `RRT1-3B-q5_k_m.gguf` - 5-bit quantization (higher quality)
- `RRT1-3B-q8_0.gguf` - 8-bit quantization (highest quality)

## Limitations

May hallucinate or provide incorrect information. Not suitable for critical decision making.

## Citation

If you use this model, please cite:

```bibtex
@misc{rrt1_3b,
  title={RRT1-3B: Fine-tuned Qwen2.5-3B-Instruct-bnb-4bit},
  author={theprint},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/theprint/RRT1-3B}
}
```