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OdiaGenAI/
odiagenAI-model-v0

Name: odiagenAI-model-v0
Availability: InStock

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License: CC 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.