News Reporter 3B LLM is based on Phi-3 Mini-4K Instruct a dense decoder-only Transformer model designed to generate high-quality text based on user prompts. With 3.8 billion parameters, the model is fine-tuned using Supervised Fine-Tuning (SFT) to align with human preferences and question answer pairs.

Base Model

We evaluated multiple off-the-shelf models, including Gemma-7B, Gemma-2B, Llama-3-8B, and Phi-3-mini-4K, and found that the Phi-3-mini-4K model performed best overall for our evaluation set. This model excels in multilingual query understanding and response generation, thanks to its 3.8 billion parameters and a 4096 context window length. Trained with over 3.3 trillion tokens, Phi-3-mini-4K stands out for its ability to be quantized to 4 bits, reducing its memory footprint to around 1.8 GB. It processes 8 to 12 tokens per second on a single T4 GPU, requiring just 3-4 GB of VRAM for inference.

Key Features:

Parameter Count: 3.8 billion.
Architecture: Dense decoder-only Transformer.
Context Length: Supports up to 4,000 tokens.
Training Data: 43.5K+ question and answer pairs curated from different News channel.

Inference

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline,set_seed

model_name = "RedHenLabs/news-reporter-3b"

tokenizer = AutoTokenizer.from_pretrained(model_name,trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype="auto", device_map="cuda")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

def test_inference(prompt):
    prefix = "Generate a concise and accurate news summary based on the following question.\n Input:"
    prompt = pipe.tokenizer.apply_chat_template([{"role": "user", "content": prefix+prompt}], tokenize=False, add_generation_prompt=True)
    outputs = pipe(prompt, max_new_tokens=512, do_sample=True, num_beams=1, temperature=0.1, top_k=50, top_p=0.95,
                   max_time= 180)
    return outputs[0]['generated_text'][len(prompt):].strip()

res = test_inference(" What is the status of the evacuations and the condition of those injured?")
print(res)

Model Benchmark

(0 Shot)	News-reporter-3b	Phi-3-mini-4k	Gemma-7b-it	Llama-2-7B	Mistral-7B-Instruct-v0.2
MMLU	69.49	69.90	64.3	45.3	59.02
ARC_C	56.40	56.14	53.2	45.9	55.89
Winogrande	74.19	73.24	68.03	69.5	73.72
Truthfulqa	50.43	66.46	44.18	57.4	53.00

Citation

@misc {lucifertrj,
   author       = { {Tarun Jain} },
   title        = { News Reporter 3B by Red Hen Lab part of Google Summer of Code 2024},
   year         = 2024,
   url          = { https://huggingface.co/RedHenLabs/news-reporter-3b },
   publisher    = { Hugging Face }
}

arxiv.org/abs/2410.07520

RichardErkhov/RedHenLabs_-_news-reporter-3b-8bits

Original model description:

News reporter 3B LLM

Model Description

Base Model

Key Features:

Inference

Model Benchmark

Citation

RichardErkhov/
RedHenLabs_-_news-reporter-3b-8bits