summarization classification translation NLP media and journalism domain specific llm

Llama2-7B-AIVision360

NewsConnect 7B (Llama2-7B-AIVision360) is a state-of-the-art, open-source chat model that stands as a beacon for technology, media, and AI news discussions. Built on the robust Llama2-7B architecture, this model has been enhanced and refined utilizing the AIVision360-8k dataset, making it a pioneer in the domain of AI news generation and interpretation.

Model Details

Dataset Utilized: AIVision360-8k

Drawing strength from the AIVision360-8k dataset, a curated collection hailing from "ainewshub.ie", this model is tailor-made for technology media and journalism. Offering structured interactions related to AI news, it captures the essence of the latest AI trends and evolutions. For a deeper dive into the dataset visit: AIVision360-8k

Model Specification

Key Features and Functionalities

Domain Specialization

The Llama2-7B-AIVision360 model is specialized in AI news, serving as a resource for AI researchers, enthusiasts, and media experts.

Model API Accessibility

Offers a straightforward Python integration for generating AI news insights.

Performance Optimisation

Efficient performance across both CPU and GPU platforms.

Data Representation

Utilises a comprehensive AI news dataset, enabling content generation akin to professional journalism standards.

Model Usage

Experience the capabilities of the Llama2-7B-AIVision360 model through a well-structured Python interface. To kick-start your exploration, follow the steps and snippets given below:

Prerequisites

1. Ensure required packages are available

import torch
import transformers
from typing import Any, Dict
from transformers import PreTrainedTokenizerFast, AutoTokenizer, 
AutoModelForCausalLM
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
    pipeline,
    logging,
)
import time

2. Initiate the model and tokenizer

model_name = "ceadar-ie/Llama2-7B-AIVision360"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", 
torch_dtype=torch.float16, trust_remote_code=True)

3. Create a function for generating text

def generate_text(input_text):
    num_words_input = len(input_text.split())

    start_time = time.time()
    
    pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=500,temperature=1, top_k=10, top_p=0.1, repetition_penalty = 1.15)
    prompt = f" [INST] {input_text} [/INST]"
    result = pipe(prompt)
    generated_text = result[0]['generated_text'].replace(prompt, "").strip()

    end_time = time.time()
    time_taken = end_time - start_time

    num_words_generated = len(generated_text.split())

    print("Generated Output:")
    print(generated_text)
    print('=' * 60 )
    print("\nNumber of Words in Input Text:", num_words_input)
    print("Number of Words in Generated Output:", num_words_generated)
    print("Time Taken to Generate Output: {:.2f} seconds".format(time_taken))

Example Evaluation and Use

Example Prompt 1:

Concisely list any recent controversies or debates in the AI community.

Generated Output:

The text discusses the latest news and developments in the AI industry, specifically highlighting controversies and debates that have been ongoing in the community. Recent controversies or debates in the AI community include:

Example Prompt 2:

List new regulations or guidelines that have been introduced globally concerning AI ethics and bias mitigation.

Generated Output:

There has been an increased focus on regulating artificial intelligence (AI) globally due to concerns about ethics, bias, and the potential for disinformation. New regulations or guidelines include:

Example Prompt 3:

What are the current market trends in the AI industry?

Generated Output:

The AI industry is rapidly evolving with notable trends such as:

Example Prompt 4:

Which sectors or industries are seeing the most rapid AI adoption right now?

Generated Output:

AI adoption is evident across various sectors, including:

Training Details

Training Hyperparameters

Model Limitations

Potential Biases: With its fine-tuning centered on AI news sources, inherent biases from these sources may reflect in the model's outputs.

Licensing

The Llama2-7B-AIVision360 model, developed in collaboration with CeADAR Connect Group, combines the licensing frameworks of both Llama2 and AIVision360. Under Meta's terms, users are granted a non-exclusive, worldwide, non-transferable, royalty-free limited license for the use and modification of Llama Materials, inclusive of the Llama2 model and its associated documentation. When redistributing, the provided Agreement and a specific attribution notice must be included. In alignment with the AIVision360 dataset's licensing, the model is also distributed under the Apache 2.0 open-source license, promoting its use and modification within the AI community, while ensuring content reliability sourced from established AI news publishers.

Out-of-Scope Use

Llama2-7B-AIVision360 is specifically tailored for AI news discussions. It is not optimized for:

Bias, Risks, and Limitations

Citation:

@misc {ceadar_2023,
	author       = { {CeADAR} },
	title        = { Llama2-7B-AIVision360 (Revision e349e9a) },
	year         = 2023,
	url          = { https://huggingface.co/ceadar-ie/Llama2-7B-AIVision360 },
	doi          = { 10.57967/hf/1069 },
	publisher    = { Hugging Face }
}

Contact:

For any further inquiries or feedback concerning Llama2-7B-AIVision360, please forward your communications to ahtsham.zafar@ucd.ie