Update README.md

README.md

@@ -9,8 +9,69 @@ datasets:
 <h1 style='text-align: left '>LLM-Tolkien</h1>
 <h3 style='text-align: left '>Write your own Lord Of The Rings story!</h3>
 
-Version 1.0 / 18 May 2023
+*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 complete article](https://medium.com/@jeremyarancio/fine-tune-an-llm-on-your-personal-data-create-a-the-lord-of-the-rings-storyteller-6826dd614fa9)
+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, return_dict=True, 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, hf_repo)
+```
+
+# 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
+)
+
+# 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"
+```