Llama-2-7b-FLASH-UK is a fine-tuned version of the powerful Llama-2-7b-chat model,fine-tuned with various custom made dataset by owner Ujjwal Kaushik to get redefined skill of text and improve its quality of text in 4 bit quantization recursively. it is designed for a wide array of natural language understanding and generation tasks. This model has been significantly optimized through 4-bit quantization using bitsandbytes, which dramatically reduces its memory footprint and boosts inference speed. This makes it an excellent choice for deployment in environments with limited computational resources.

Developed by Ujjwal Kaushik (ujjwal52), this model retains the strong conversational capabilities of its base Llama-2 architecture, offering coherent, contextually relevant, and creative responses across various domains with great human interactive text .

Why Choose This Model?

Exceptional Efficiency with 4-bit Quantization

Leveraging state-of-the-art 4-bit quantization, this model boasts an estimated memory footprint of approximately 3.89 GB. This allows it to run effectively on consumer-grade GPUs or systems with restricted VRAM, providing high-quality text generation without demanding extensive hardware resources.

Robust Conversational AI

Built upon the solid foundation of Llama-2-7b-chat, this model excels in engaging and natural dialogues. It is adept at following instructions, generating creative content, and maintaining coherent conversations, making it versatile for various interactive AI applications.

Key Features

Base Model: Llama-2-7b-chat
Quantization: 4-bit (BitsAndBytesConfig with nf4, torch.float16 compute dtype)
Optimized for: General conversation, instruction following, creative writing, and diverse natural language tasks,text generation.
Memory Footprint: Approximately 3.89 GB (quantized).
Repository: ujjwal52/Llama-2-7b-FLASH-UK

How to Use

To integrate this model into your project, you can load it directly from the Hugging Face Hub using the transformers library:

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_name = "ujjwal52/Llama-2-7b-FLASH-UK"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=False
)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example for text generation
from transformers import pipeline

prompt = "Write a short story about an AI assistant that helps a human discover a new planet."
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=500)
result = pipe(f"<s>[INST] {prompt} [/INST]")
print(result[0]['generated_text'])

Training Details

This model underwent fine-tuning on various datasets to enhance its general linguistic understanding and generation capabilities. The 4-bit quantization process was meticulously applied to ensure a balance between efficiency and maintaining high model performance.

Disclaimer

This model is provided as a research artifact and should be used with appropriate discretion. While efforts have been made to ensure its quality and safety, it may occasionally generate content that is inaccurate, biased, or potentially harmful. For sensitive applications, implementing robust content moderation and human review mechanisms is strongly recommended.