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--- |
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license: apache-2.0 |
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
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tags: |
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- TinyLlama |
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- QLoRA |
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- Politics |
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- News |
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- sft |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# TinyNewsLlama-1.1B |
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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. |
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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 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. |
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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 |
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import transformers |
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import torch |
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model = "h4rz3rk4s3/TinyNewsLlama-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 an experienced journalist.", |
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}, |
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{"role": "user", "content": "Write a short article 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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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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device_map="auto", |
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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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``` |
