library_name: transformers
tags: []

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Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Uses

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Recommendations

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How to Get Started with the Model

Use the code below to get started with the model.

Import important libraries

import transformers
import torch
from transformers import pipeline
import accelerate

Prepare model and tokenizer

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "pankaj9075rawat/DevsDoCode_LLama-3-8b-Uncensored"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

Build Pipeline for text generation

pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    # model_kwargs={"torch_dtype": torch.bfloat16},
    # device="cuda",
    # device_map="auto",
    # token=access_token
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

Build response function

def get_response(
          query, message_history=[], max_tokens=128, temperature=1.1, top_p=0.9
      ):
    user_prompt = message_history + [{"role": "user", "content": query}]
    prompt = pipeline.tokenizer.apply_chat_template(
        user_prompt, tokenize=False, add_generation_prompt=True
    )
    # print("prompt before coversion: ", user_prompt)
    # print("prompt after conversion: ", prompt)
    outputs = pipeline(
        prompt,
        max_new_tokens=max_tokens,
        eos_token_id=terminators,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
    )
    response = outputs[0]["generated_text"][len(prompt):]
    return response, user_prompt + [{"role": "assistant", "content": response}]

Build chat on notebook itself (define a system prompt variable)

convers = [{"role": "system", "content": system_instruction}]


def chat():
    global convers 
    response, convers = get_init_AI_response(convers)
    print("response:", response)

    while True:
        user_input = input("enter chat")
        if user_input.lower() in ["exit", "quit"]:
            return {"response": "Exiting the chatbot. Goodbye!"}

        response, convers = get_response(user_input, convers)
        print("response:", response)

chat()

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Training Details

Training Data

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Training Procedure

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Evaluation

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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