README.md

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

# TinyParlaMintLlama-1.1B

TinyParlaMintLlama-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) using a sample of a concentrated version of the English [ParlaMint] (https://www.clarin.si/repository/xmlui/handle/11356/1864) Dataset using QLoRA. The model was fine-tuned for ~12h on one A100 40GB on ~100M 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 speeches from the **Austrian**, **Danish**, **French**, **British**, **Hungarian**, **Dutch**, **Norwegian**, **Polish**, **Swedish** and **Turkish** Parliament. The concentrated ParlaMint 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, AutoModelForCausalLM
from accelerate import Accelerator
import transformers
import torch
model = "h4rz3rk4s3/TinyParlaMintLlama-1.1B"
messages = [
    {
        "role": "system",
        "content": "You are a professional writer of political speeches.",
    },
    {"role": "user", "content": "Write a short speech 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)
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"])
```