Model Card for Model ID

Model Details

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.

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

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()

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

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

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]