README.md

---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- TinyLlama
- QLoRA
- Politics
- News
- sft
language:
- en
pipeline_tag: text-generation
---
# UPDATE March, 17th: Changed quantization for the merge of the adapter and the original model. 

# TinyNewsLlama-1.1B

TinyNewsLlama-1.1B is a QLoRA SFT fine-tune of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) using a sample of a concentrated version of the [bigNews] (https://paperswithcode.com/dataset/bignews) Dataset. The model was fine-tuned for ~12h on one A100 40GB on ~125M tokens.

The goal of this project is to study the potential for improving the domain-specific (in this case political) knowledge of small (<3B) LLMs by concentrating the training datasets TF-IDF in respect to the underlying Topics found in the origianl Dataset.

The used training data contains political news articles from **The New York Times**, **USA Today** and **The Washington Times**. The concentrated BigNews Dataset as well as more information about the used sample will soon be added.


## 💻 Usage

```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch

model = "h4rz3rk4s3/TinyNewsLlama-1.1B"
messages = [
    {
        "role": "system",
        "content": "You are a an experienced journalist.",
    },
    {"role": "user", "content": "Write a short article on Brexit and it's impact on the European Union."},
]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```