---
license: bigscience-bloom-rail-1.0
language: '-en'
library_name: adapter-transformers
datasets:
- JeremyArancio/lotr-book
---
<h1 style='text-align: left '>LLM-Tolkien</h1>
<h3 style='text-align: left '>Write your own Lord Of The Rings story!</h3>

*Version 1.1 / 23 May 2023*

# Description

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)".

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)

[Github repository](https://github.com/jeremyarancio/llm-rpg/tree/main)

# Load the model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel

# Import the model
config = PeftConfig.from_pretrained("JeremyArancio/llm-tolkien")
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, "JeremyArancio/llm-tolkien")
```

# Run the model

```python
prompt = "The hobbits were so suprised seeing their friend"

inputs = tokenizer(prompt, return_tensors="pt")
tokens = model.generate(
    **inputs,
    max_new_tokens=100,
    temperature=1,
    eos_token_id=tokenizer.eos_token_id,
    early_stopping=True
)
print(tokenizer.decode(tokens[0]))

# The hobbits were so suprised seeing their friend again that they did not 
# speak. Aragorn looked at them, and then he turned to the others.</s>
```

# Training parameters

```python
# Dataset
context_length = 2048

# Training
model_name = 'bigscience/bloom-3b'
lora_r = 16 # attention heads
lora_alpha = 32 # alpha scaling
lora_dropout = 0.05
lora_bias = "none"
lora_task_type = "CAUSAL_LM" # set this for CLM or Seq2Seq

## Trainer config
per_device_train_batch_size = 1 
gradient_accumulation_steps = 1
warmup_steps = 100 
num_train_epochs=3
weight_decay=0.1
learning_rate = 2e-4 
fp16 = True
evaluation_strategy = "no"
```