README.md

---
license: cc-by-nc-sa-4.0
language:
- or
tags:
- code
---
# Model Card for Model ID

[![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC_BY--NC--SA_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)

## Model description

odiagenAI-model-v1 is based on Llama-7b and finetuned with 171k Odia instruction set. The instruction set is translated data from open-source resources, resulting in good Odia instruction understanding and response generation capabilities. 

The code of Odia data generation and other detailed information can be found in our Github project repository: https://github.com/OdiaGenAI/GenerativeAI_and_LLM_Odia.
This repo contains a low-rank adapter for LLaMA-7b fit on the Stanford Alpaca dataset.


## Training hyper-parameters
| Parameter | Value |
| ------ | ------ |
| Batch size | 128 |
| Learning rate | 3e-4 |
| Epochs | 3 |
|Cutoff length | 256 |
|Weight_decay | 0.001 |
|Warmup_rate | 0.1 |
|LR_scheduler | linear |
|Lora r | 16 |
|Lora target modules | (q_proj, k_proj, v_proj, o_proj) |


Model can be easily loaded with AutoModelForCausalLM.
``` python
import torch
from peft import PeftModel
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel, PeftConfig
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig

base_model_path = "meta-llama/Llama-2-7b-hf"
adapter_path = "OdiaGenAI/odiagenAI-model-v1"

tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16,
)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_path,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

model = PeftModel.from_pretrained(base_model, adapter_path)

instruction = "ଭାରତ ବିଷୟରେ କିଛି କୁହନ୍ତୁ"

device = "cuda" if torch.cuda.is_available() else "cpu"

inputs = tokenizer(instruction, return_tensors="pt").to(device)
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
    temperature=0.1,
    top_p=0.75,
    top_k=40,
    num_beams=4,
)
with torch.no_grad():
    generation_output = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=128,
    )
s = generation_output.sequences[0]
output = tokenizer.decode(s)
print(output)

```


Instructions for running it can be found at https://github.com/OdiaGenAI/GenerativeAI_and_LLM_Odia.

### Licensing Information

This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg

### Citation Information

If you find this helpful repository, please consider giving 👏 and citing:

```
@misc{OdiaGenAI,
  author = {Shantipriya Parida and Sambit Sekhar and Subhadarshi Panda and Soumendra Kumar Sahoo and Swateek Jena and Abhijeet Parida and Arghyadeep Sen and Satya Ranjan Dash and Deepak Kumar Pradhan},
  title = {OdiaGenAI: Generative AI and LLM Initiative for the Odia Language},
  year = {2023},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/OdiaGenAI}},
}
```

### Contributions

- Shantipriya Parida
- Sambit Sekhar