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--- |
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license: bigscience-bloom-rail-1.0 |
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language: '-en' |
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library_name: adapter-transformers |
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datasets: |
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- JeremyArancio/lotr-book |
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--- |
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<h1 style='text-align: left '>LLM-Tolkien</h1> |
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<h3 style='text-align: left '>Write your own Lord Of The Rings story!</h3> |
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*Version 1.1 / 23 May 2023* |
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# Description |
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This LLM is fine-tuned on [Bloom-3B](https://huggingface.co/bigscience/bloom-3b) with texts extracted from the book "[The Lord of the Rings](https://gosafir.com/mag/wp-content/uploads/2019/12/Tolkien-J.-The-lord-of-the-rings-HarperCollins-ebooks-2010.pdf)". |
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The article: [Fine-tune an LLM on your personal data: create a “The Lord of the Rings” storyteller.](https://medium.com/@jeremyarancio/fine-tune-an-llm-on-your-personal-data-create-a-the-lord-of-the-rings-storyteller-6826dd614fa9) |
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[Github repository](https://github.com/jeremyarancio/llm-rpg/tree/main) |
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# Load the model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftConfig, PeftModel |
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# Import the model |
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config = PeftConfig.from_pretrained("JeremyArancio/llm-tolkien") |
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model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto') |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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# Load the Lora model |
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model = PeftModel.from_pretrained(model, "JeremyArancio/llm-tolkien") |
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``` |
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# Run the model |
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```python |
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prompt = "The hobbits were so suprised seeing their friend" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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tokens = model.generate( |
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**inputs, |
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max_new_tokens=100, |
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temperature=1, |
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eos_token_id=tokenizer.eos_token_id, |
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early_stopping=True |
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) |
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print(tokenizer.decode(tokens[0])) |
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# The hobbits were so suprised seeing their friend again that they did not |
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# speak. Aragorn looked at them, and then he turned to the others.</s> |
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``` |
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# Training parameters |
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```python |
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# Dataset |
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context_length = 2048 |
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# Training |
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model_name = 'bigscience/bloom-3b' |
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lora_r = 16 # attention heads |
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lora_alpha = 32 # alpha scaling |
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lora_dropout = 0.05 |
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lora_bias = "none" |
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lora_task_type = "CAUSAL_LM" # set this for CLM or Seq2Seq |
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## Trainer config |
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per_device_train_batch_size = 1 |
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gradient_accumulation_steps = 1 |
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warmup_steps = 100 |
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num_train_epochs=3 |
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weight_decay=0.1 |
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learning_rate = 2e-4 |
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fp16 = True |
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evaluation_strategy = "no" |
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``` |
