---
license: apache-2.0
tags:
- TinyLlama
- QLoRA
- Politics
- EU
- News
- sft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
---

# TinyPoliticaLlama-1.1B

TinyPoliticaLlama-1.1B is a SFT fine-tune of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) and a sister model of [h4rz3rk4s3/TinyParlaMintLlama-1.1B](https://huggingface.co/h4rz3rk4s3/TinyParlaMintLlama-1.1B) and [h4rz3rk4s3/TinyNewsLlama-1.1B](https://huggingface.co/h4rz3rk4s3/TinyNewsLlama-1.1B).
This model was fine-tuned for ~24h on one A100 40GB on ~225M tokens on the training corpora of both her sister models.

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 speeches from the **Austrian**, **Danish**, **French**, **British**, **Hungarian**, **Dutch**, **Norwegian**, **Polish**, **Swedish** and **Turkish** Parliament, as well as political news articles from **The New York Times**, **USA Today** and **The Washington Times**.


## 💻 Usage

```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
from accelerate import Accelerator
import transformers
import torch
model = "h4rz3rk4s3/TinyPoliticaLlama-1.1B"
messages = [
    {
        "role": "system",
        "content": "You are an experienced journalist in the political domain and an expert of European politics.",
    },
    {"role": "user", "content": "Write a short article explaining how the French yellow vest protests started, how they developed over time and how the French Government reacted to the protests."},
]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model = AutoModelForCausalLM.from_pretrained(
    model, trust_remote_code=True, device_map={"": Accelerator().process_index}
)

pipeline = transformers.pipeline(
    "text-generation",
    tokenizer=tokenizer,
    model=model,
    torch_dtype=torch.float16,
    device_map={"": Accelerator().process_index},
)
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"])
```