Update README.md

README.md

@@ -9,34 +9,53 @@ base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
 license: other
 ---
 
-# Model Trained Using AutoTrain
+# LoRA Model: Fine-Tuned on Argument to Logical Structure Dataset
+
+Model Description
+
+This model is a LoRA (Low-Rank Adaptation) fine-tuned version of the LLaMA 3.1 8B Instruct model. It has been specifically trained to convert natural language arguments into structured logical forms, including premises, conclusions, and formal symbolic proofs.
+
+Training Details
+
+	•	Base Model: LLaMA 3.1 8B Instruct
+	•	LoRA Rank: 16
+	•	LoRA Alpha: 32
+	•	Epochs: 15
+	•	Batch Size: 2
+	•	Dataset: The model was trained on a custom dataset designed to map natural language arguments to logical structures. For more details, visit the dataset link: https://huggingface.co/datasets/ergotts/propositional-logic.
 
-This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
 
 # Usage
 
 ```python
 
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+base_model = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+adapter_model = "ergotts/llama_3.1_8b_prop_logic_ft"
 
-model_path = "PATH_TO_THIS_REPO"
+model = AutoModelForCausalLM.from_pretrained(base_model)
+model = PeftModel.from_pretrained(model, adapter_model)
+tokenizer = AutoTokenizer.from_pretrained(base_model)
 
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-model = AutoModelForCausalLM.from_pretrained(
-    model_path,
-    device_map="auto",
-    torch_dtype='auto'
-).eval()
 
-# Prompt content: "hi"
+model = model.to("cuda")
+model.eval()
+
 messages = [
-    {"role": "user", "content": "hi"}
+    {"role": "user", "content": "Hi!"},
 ]
 
 input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
-output_ids = model.generate(input_ids.to('cuda'))
+output_ids = model.generate(input_ids.to('cuda'),max_new_tokens=1000)
 response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
 
 # Model response: "Hello! How can I assist you today?"
 print(response)
-```
+
+```
+
+# Model Trained Using AutoTrain
+
+This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).