53 lines
2.1 KiB
Markdown
53 lines
2.1 KiB
Markdown
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---
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license: apache-2.0
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tags:
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- TinyLlama
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- QLoRA
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- Politics
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- EU
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- sft
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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---
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# TinyParlaMintLlama-1.1B
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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.
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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.
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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.
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from accelerate import Accelerator
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import transformers
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import torch
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model = "h4rz3rk4s3/TinyParlaMintLlama-1.1B"
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messages = [
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{
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"role": "system",
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"content": "You are a professional writer of political speeches.",
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},
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{"role": "user", "content": "Write a short speech on Brexit and it's impact on the European Union."},
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]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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model = AutoModelForCausalLM.from_pretrained(
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model, trust_remote_code=True, device_map={"": Accelerator().process_index}
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)
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pipeline = transformers.pipeline(
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"text-generation",
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tokenizer=tokenizer,
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model=model,
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torch_dtype=torch.float16,
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device_map={"": Accelerator().process_index},
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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