peft safetensors region:us
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
library_name: peft
---

# Qwen2.5-3B-Instruct with LoRA Adapter

This model is a fine-tuned version of the `unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit` using Parameter-Efficient Fine-Tuning (PEFT) with the LoRA method.

## Model Details

### Model Description

This model applies LoRA (Low-Rank Adaptation) to the Qwen2.5-3B-Instruct base model, targeting key projection modules for efficient fine-tuning. It is quantized to 4-bit precision using the Unsloth library to optimize for inference performance on lower-resource hardware.

- **Developed by:** [montebello.ai](https://www.montebello.ai/)
- **Funded by:** Bootstrapped 4 Life
- **Model type:** Causal Language Model with LoRA Adapter
- **Language(s) (NLP):** English (primary), multilingual support for some other languages (research ongoing).
- **License:** MIT
- **Finetuned from model:** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit

### Model Sources

- **Demo [optional]:** [COMING SOON]

## Uses

### Direct Use

This model is designed for natural language understanding and generation tasks, including:

- Conversational AI
- Summarization
- Text completion
- Question answering

### Downstream Use

Fine-tuning the model for specific NLP tasks, such as custom domain conversational agents.

### Out-of-Scope Use

- Real-time critical systems requiring guaranteed safety and accuracy.
- Generating content for sensitive domains without human oversight.

## Bias, Risks, and Limitations

- The base model may contain biases present in the original training data.
- The model is not fine-tuned for safety-critical applications.
- Limitations include possible hallucinations in generative outputs and language biases.

### Recommendations

- Perform task-specific evaluations before deploying the model.
- Include human-in-the-loop for critical applications.

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit")
base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "path_to_your_adapter")

# Example inference
input_text = "Your input text here."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```

## Training Details

### Training Procedure

- **Preprocessing:** Tokenization with AutoTokenizer.
- **Training regime:** bf16 mixed precision with LoRA fine-tuning.
- **LoRA Configuration:**
  - `lora_alpha`: 64
  - `r`: 64
  - `target_modules`: ['v_proj', 'o_proj', 'up_proj', 'gate_proj', 'down_proj', 'q_proj', 'k_proj']
  - `bias`: none

### Training Data

This model was trained on the [GSM8K dataset](https://github.com/openai/grade-school-math) from OpenAI.

## Environmental Impact

- **Hardware Type:** T4 TPU
- **Hours used:** 2.5 hours

## Technical Specifications

### Model Architecture and Objective

- Transformer-based causal language model using LoRA for efficient adaptation.

### Compute Infrastructure

- **Hardware:** [Specify GPU/CPU setup]
- **Software:**
  - Python 3.x
  - Transformers library
  - PEFT 0.14.0

## Citation [optional]

**BibTeX:**

```
@misc{your2025model,
  title={Qwen2.5-3B-Instruct with LoRA Adapter},
  author={Kenneth Hamilton},
  year={2025}
}
```

**APA:**

Your Name. (2025). *Qwen2.5-3B-Instruct with LoRA Adapter*. Retrieved from Your repository link.

## Model Card Contact

- **Contact:** [Your contact information]

### Framework versions

- PEFT 0.14.0

