

# Model Card for Model ID


## Model description

OdiaGen is based on Llama-7b and finetuned with 52k English data from the open-source Stanford-Alpaca, resulting in good English 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 | 3 |
|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

assert (
    "LlamaTokenizer" in transformers._import_structure["models.llama"]
), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig

tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")

BASE_MODEL = "decapoda-research/llama-7b-hf"
LORA_WEIGHTS = "tloen/alpaca-lora-7b"


model = LlamaForCausalLM.from_pretrained(
        BASE_MODEL,
        load_in_8bit=False,
        torch_dtype=torch.float16,
        device_map="auto",
    )
model = PeftModel.from_pretrained(
        model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True
    )

def generate_prompt(instruction, input=None):
    if input:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Input:
{input}
### Response:"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:"""

prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
    temperature=0.1,
    top_p=0.75,
    top_k=40,
    num_beams=4,
    **kwargs,
)
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.split("### Response:")[1].strip())


```


Instructions for running it can be found at https://github.com/shantipriyap/OdiaGenAI.
{"language": ["en"], "license": "cc-by-4.0"}
en license:cc-by-4.0 region:us
TAGS
#en #license-cc-by-4.0 #region-us 

Model Card for Model ID
=======================


Model description
-----------------


OdiaGen is based on Llama-7b and finetuned with 52k English data from the open-source Stanford-Alpaca, resulting in good English instruction understanding and response generation capabilities.


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


Training hyper-parameters
-------------------------



Model can be easily loaded with AutoModelForCausalLM.


Instructions for running it can be found at URL



TAGS
#en #license-cc-by-4.0 #region-us 

TAGS
#en #license-cc-by-4.0 #region-us 
