Fix all references to use merged model, remove broken API section

README.md

@@ -9,7 +9,7 @@ tags:
 - dense-responses
 - self-optimization
 - representation-engineering
-base_model: NousResearch/Hermes-3-Llama-3.1-8B
+base_model: LoganResearch/ARC-Base-8B-Condensed
 ---
 ![ARC Banner](banner.svg)
 
@@ -18,9 +18,9 @@ base_model: NousResearch/Hermes-3-Llama-3.1-8B
 A closed-loop control system that uses internal state predictability to improve response efficiency without collapsing.
 
 **Author:** Logan Matthew Napolitano  
-**Base Model:** NousResearch/Hermes-3-Llama-3.1-8B  
+**Base Model:** LoganResearch/ARC-Base-8B-Condensed  
 **License:** CC BY 4.0  
-**Code:** 7,111 lines | **Weights:** ~6.5 GB
+**Code:** 7,111 lines | **Weights:** ~16 GB
 
 ---
 
@@ -155,39 +155,6 @@ A/B checkpoint comparison with automatic rollback on quality drops > 0.05.
 
 ---
 
-## API Integration
-
-For developers integrating ARC into their own applications:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-import torch
-
-base = AutoModelForCausalLM.from_pretrained(
-    "NousResearch/Hermes-3-Llama-3.1-8B",
-    torch_dtype=torch.float16,
-    device_map="auto",
-    load_in_4bit=True
-)
-
-model = PeftModel.from_pretrained(
-    base, 
-    "LoganResearch/ARC-Base-8B-Condensed",
-    subfolder="dense_checkpoints/step_100"
-)
-
-tokenizer = AutoTokenizer.from_pretrained("NousResearch/Hermes-3-Llama-3.1-8B")
-
-prompt = "<|im_start|>user\nWhat is recursion?<|im_end|>\n<|im_start|>assistant\n"
-inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-output = model.generate(**inputs, max_new_tokens=50)
-print(tokenizer.decode(output[0], skip_special_tokens=True))
-```
-
-Note: For full dense output with CF-HoT steering, use the main engine (`ubermenschetien_v2_full.py`).
-
----
 
 ## Training From Scratch