README.md

---
language:
- or
---

# 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-v0 is based on Llama-7b and finetuned with 52k Odia translated data from the open-source Stanford-Alpaca, 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/shantipriyap/OdiaGenAI.
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 | 2 |
|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-v0"

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/shantipriyap/OdiaGenAI.