Update README.md

README.md

@@ -97,7 +97,7 @@ The Final Model achieves the highest F1 score across all iterations, demonstrati
 **QLoRA advantages:**
 - Adapts a generative LLM to patent language with only 0.53% of parameters trained
 - Enables a domain-aware Judge that understands Y02 classification logic
-- 4-bit quantization fits 8B model on a free 16 GB T4 GPU
+- 4-bit quantization fits 8B model on a free 15 GB T4 GPU
 
 **MAS + HITL advantages:**
 - Debate structure surfaces disagreements that single-model approaches miss
@@ -107,7 +107,6 @@ The Final Model achieves the highest F1 score across all iterations, demonstrati
 **Limitations:**
 - DirectML (AMD GPU) not fully supported by sentence-transformers training — fell back to CPU
 - torchao 0.16.0 conflicts with transformers lazy loader in certain environments
-- LM Studio local inference adds latency (~3–5s per agent call)
 
 
 ## Repository Contents
@@ -120,6 +119,7 @@ The Final Model achieves the highest F1 score across all iterations, demonstrati
 | `final_classifier.joblib` | Serialised final Logistic Regression classifier |
 | `qlora_outputs.zip` | QLoRA adapter weights (`qlora_green_patent_adapter/`) |
 | `Part C Step 1.ipynb` | Colab notebook for QLoRA fine-tuning |
+| `Debate transcripts` | All debate transcrips for MAS |
 
 
 ## Related Repositories