Update README.md with corrected model usage plus some code snippets

README.md

@@ -97,12 +97,12 @@ Elenky is designed for philosophical dialogue and reflective questioning, especi
 To load the model using the Hugging Face Transformers library:
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-model_name = "cg2wc/elenky"
+model_name = "Cglowacki/elenky"
 
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
 
 USER_PROMPT = "2 + 2 = 4"
 prompt = f"""<|system|>
@@ -121,6 +121,54 @@ print(response)
 
 Make sure to manage conversation history by prepending previous turns as the dialogue continues.
 
+### Helpful Code
+
+#### Extracting a single turn from output
+
+Below is example code for extracting a single assistant response from the model output:
+
+```python
+def extract_template_response(response: str, prompt: str):
+    single_turn_output = ""
+    new_content = response[len(prompt):]
+    user_token_idx = new_content.find("<|user|>")
+    if user_token_idx != -1:
+        single_turn_output = new_content[:user_token_idx]
+    else:
+        single_turn_output = new_content
+    return single_turn_output
+```
+
+#### Generating a model prompt from a list
+
+Below is example code for turning a list of turns into a formatted text prompt:
+
+```python
+from typing import List
+
+chat = [
+{"role": "user", "content":"2 + 2 = 4"},
+{"role": "assistant", "content": "If 2 + 2 equals 4, what does it mean for the concept of truth in mathematics? Is it absolute?"},
+{"role": "user", "content": "Mathematical truths are absolute."},
+{"role": "assistant", "content": "If that's the case, how do you explain the existence of mathematical contradictions in certain systems? Does that undermine the idea of absolute truth in mathematics?"}
+]
+
+SYS_PROMPT = """<|system|>
+You, assistant, are a philosophy expert engaging in a socratic discussion about a particular philosphical concept with me, user. 
+The first speaker, user, will seek to make claims about a stance. 
+The second speaker, assistant, will play devil's advocate and respond with a question about what user has said that seeks to expand the conversation."""
+
+def generate_fulltext_prompt(chat: List[dict]):
+    lines = []
+    for turn in chat:
+        if turn['role'] == 'user':
+            lines.append(f"<|user|> {turn['content']}")
+        elif turn['role'] == 'assistant':
+            lines.append(f"<|assistant|> {turn['content']}")
+    return "\n".join([SYS_PROMPT,*lines])
+```
+
+
 ## 📝 Prompt Format
 
 Elenky uses a custom prompting strategy that leverages elements of chain-of-thought (CoT) prompting. Each interaction tells the model to act as a philosophy expert committed to open inquiry. Rather than aiming for quick answers, it’s guided to ask probing questions, reveal contradictions, and deepen the conversation.