Hugging Face Model - Bengali Finetuned

This repository contains a Hugging Face model that has been fine-tuned on a Bengali dataset. The model uses the peft library for generating responses.

Usage

To use the model, first import the necessary libraries:

from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig

Next, load the tokenizer and model:

tokenizer = LlamaTokenizer.from_pretrained("yahma/llama-7b-hf")
model = LlamaForCausalLM.from_pretrained(
    "yahma/llama-7b-hf",
    load_in_8bit=True,
    device_map="auto",
)

Then, load the PeftModel with the specified pre-trained model and path to the peft model:

model = PeftModel.from_pretrained(model, "./bengali-dolly-alpaca-lora-7b")

Next, define a function to generate a prompt:

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:"""

Finally, define a function to evaluate the model:

generation_config = GenerationConfig(
    temperature=0.1,
    top_p=0.75,
    num_beams=4,
)

def evaluate(model, instruction, input=None):
    prompt = generate_prompt(instruction, input)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].cuda()
    generation_output = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=256
    )
    for s in generation_output.sequences:
        output = tokenizer.decode(s)
        print("Response:", output.split("### Response:")[1].strip())

instruct =input("Instruction: ")
evaluate(model, instruct)

To generate a response, simply run the evaluate function with an instruction and optional input:

instruct = "Write a response that appropriately completes the request."
input = "This is a sample input."
evaluate(model, instruct, input)

This will output a response that completes the request.