Update README.md

README.md

@@ -14,11 +14,11 @@ metrics:
 - cosine_similarity
 ---
 
-# 🪐 DiffReaper-5 (Cropmark v2)
+# DiffReaper-5
 
 DiffReaper-5 is a **Conditioned Diffusion Large Language Model (DLLM)** designed for high-throughput, parallel conversational text generation. Unlike standard autoregressive models (GPT-style), DiffReaper-5 operates in the continuous latent embedding space, denoising an entire response sequence in parallel.
 
-## 🔬 Model Details
+## Model Details
 
 - **Architecture:** Custom 12-layer Mercury-inspired Transformer.
 - **Task:** Conditioned Text Diffusion (Prompt-Response).
@@ -26,15 +26,7 @@ DiffReaper-5 is a **Conditioned Diffusion Large Language Model (DLLM)** designed
 - **Training Objective:** Cosine Similarity Regression (Directional Loss).
 - **Sampling:** 10-step iterative parallel denoising.
 
-## 🚀 Autonomous Training State
-
-This model is currently in **Autonomous Growth Mode**. It is training on an RTX 3090 cluster with the following parameters:
-- **Conditioning:** Hard-prompt conditioning (32 tokens).
-- **Generation Window:** 32 tokens (parallel).
-- **Optimizer:** AdamW with a learning rate of 1e-4.
-- **Sync:** Auto-checkpointing every 2,500 steps to this repository.
-
-## 🛠️ Usage (Inference)
+## Usage (Inference)
 
 Unlike autoregressive models, DiffReaper-5 generates the entire response in parallel through iterative denoising. Use the following logic to run inference:
 
@@ -69,7 +61,7 @@ def generate(model, tokenizer, prompt, steps=10):
 # model.load_state_dict(torch.load("cropmark_latest.pt"))
 ```
 
-## 🎯 Fine-tuning
+## Fine-tuning
 
 To fine-tune DiffReaper-5 on a custom dataset:
 1. **Objective:** Use `1 - F.cosine_similarity` between predicted and target embeddings.
@@ -81,6 +73,3 @@ To fine-tune DiffReaper-5 on a custom dataset:
 The model's progress is monitored via the **Cropmark Diagnostic**. 
 - **Cropmark** tests the model's ability to manifest a response (e.g., "I am good, how are you?") from pure Gaussian noise given a fixed prompt.
 - Results are logged in `checkpoint_log.txt` and uploaded periodically.
-
----
-*Created by Darwin & Clawd.*