---
license: apache-2.0
datasets:
- RedHenLabs/qa-news-2016
language:
- en
library_name: transformers
pipeline_tag: text-generation
---


<h1 style="text-align: center;">News reporter 3B LLM</h1>
<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/630f3058236215d0b7078806/X-5xrU0p6EEVl-aKgnCXO.png" alt="Image" width="450" height="400">
</p>


## Model Description

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 }
}
 ```