transformers safetensors llama text-generation text-generation-inference unsloth trl sft conversational en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: meta-llama/Llama-3.2-3B-Instruct
language:
- en
- or
license: apache-2.0
tags:
- text-generation-inference
- transformers
- llama
- sft
---

# Llama-3.2-3B-Instruct-Odia
This model is a fine-tuned version of Meta's [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) optimized for Odia language generation and understanding. It was trained using the Unsloth library to efficiently fine-tune the model with LoRA on the Alpaca-Odia dataset.

## Model Description

- **Base Model**: Meta's Llama-3.2-3B-Instruct
- **Training Technique**: QLoRA fine-tuning with Unsloth
- **Training Dataset**: Alpaca-Odia with data augmentation
- **Language Capabilities**: Odia (ଓଡ଼ିଆ) and English

This model has been specifically optimized to:

- Understand instructions in both English and Odia
- Generate fluent responses in Odia
- Respond to various queries with culturally appropriate Odia text

## Intended Uses

- Odia language generation and conversation
- Question answering in Odia

## Training Details
The model was fine-tuned using:

- Parameter-efficient fine-tuning with QLoRA (r=32)
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Learning rate: 2e-4 with linear scheduler
- Training with 4-bit quantization for memory efficiency
- Data augmentation to improve instruction following in both languages

## Sample Usage
```python
from transformers import pipeline
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sumankumarbhadra/Llama-3.2-3B-Instruct-Odia"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
    low_cpu_mem_usage=True
)

pipe = pipeline(
    "text-generation", 
    model=model,
    tokenizer=tokenizer
)

messages = [
    {"role": "user", "content": "Hi, How are you?"},
]

result = pipe(
    messages, 
    do_sample=True,
    max_length=512,
    num_return_sequences=1,
    batch_size=1
)

assistant_response = result[0]['generated_text'][-1]['content']
print(assistant_response)
```

## Limitations

- The model's Odia language capabilities are limited by the size and quality of the training dataset
- Performance varies based on the complexity and domain of the query
- May occasionally mix English and Odia in responses for certain prompts

## Ethical Considerations
This model aims to increase accessibility to AI technologies for Odia speakers. When using this model, please ensure:

- Respect for cultural sensitivities and local contexts
- Awareness of potential biases in generated content
- Proper content moderation for deployed applications
- Acknowledgment of limitations when using for critical applications

## Acknowledgements

- Meta for releasing the Llama 3.2 model family
- Unsloth team for their efficient fine-tuning library