docs: add benchmark charts, social banner, and updated repo links

README.md

@@ -31,13 +31,15 @@ widget:
 
 # AViGPT: 183M Parameter Core with Native NVMe Hardware Bus
 
+![AViGPT Banner](assets/social_preview.png)
+
 **AViGPT** is a 183-million parameter autoregressive language model designed and pretrained from scratch by **Avinash Ricky Yadlapalli**.
 
 Rather than increasing parameter count to memorize factual records inside dense neural weights, AViGPT separates syntactic reasoning from factual storage. It couples a compact 183M reasoning core with a dedicated local NVMe SSD hardware memory bus. The model emits explicit control tokens to pause inference, execute sub-millisecond SQLite FTS5 full-text lookups on local storage, inject verified records into context, and complete generations with verified factual precision.
 
-* **Author:** Avinash Ricky Yadlapalli ([@Avinashricky211](https://huggingface.co/Avinashricky211))
-* **Interactive Demo:** [AViGPT Studio Space](https://huggingface.co/spaces/Avinashricky211/AViGPT-Studio)
-* **Code Repository:** [GitHub - Avinashricky211/AViGPT](https://github.com/Avinashricky211/AViGPT)
+* **Author:** Avinash Ricky Yadlapalli ([@AvinashRicky](https://huggingface.co/AvinashRicky))
+* **Code Repository:** [GitHub - Avinashricky211/AviGPT](https://github.com/Avinashricky211/AviGPT)
+* **License:** MIT Open Source License
 
 ---
 
@@ -56,7 +58,7 @@ Rather than increasing parameter count to memorize factual records inside dense
 | **Alignment Dataset** | 25,850 multi-step hardware trajectories |
 | **Storage Engine** | Local SQLite 3 FTS5 (WAL mode, normal synchronous disk writes) |
 | **SSD Latency** | 1.18 milliseconds (NVMe average read latency) |
-| **RAM Footprint** | ~0.4 GB (runs comfortably on free-tier CPU or edge devices) |
+| **RAM Footprint** | ~0.4 GB (runs comfortably on CPU or edge devices) |
 
 ---
 
@@ -93,6 +95,25 @@ User Instruction
 
 ---
 
+## Empirical Benchmarks & Performance Charts
+
+### 1. Training Convergence Curve
+Cross-entropy loss declined from 3.3698 to 0.6935 over 1,800 steps on an NVIDIA T4 GPU:
+
+![Training Loss Convergence](assets/charts/loss_convergence.png)
+
+### 2. External Retrieval Latency (NVMe Bus vs. Network RAG)
+Direct NVMe storage retrieval operates in 1.18 milliseconds, compared to 500 to 1,500 milliseconds for network-based RAG architectures:
+
+![Memory Latency Comparison](assets/charts/memory_latency_comparison.png)
+
+### 3. Hardware Footprint Comparison
+AViGPT operates with a 0.4 GB memory footprint, running entirely on consumer CPUs without requiring dedicated GPU accelerators:
+
+![Hardware Footprint Comparison](assets/charts/vram_footprint_comparison.png)
+
+---
+
 ## Hardware & Efficiency Comparison
 
 | System | Parameters | Minimum Hardware | Retrieval Latency | Knowledge Updates |
@@ -114,7 +135,7 @@ You can load and query the model directly via the `transformers` library:
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_id = "Avinashricky211/AViGPT"
+model_id = "AvinashRicky/AViGPT"
 
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
@@ -141,17 +162,20 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=False))
 To run AViGPT with active sub-millisecond SSD queries and arithmetic execution:
 
 ```bash
-git clone https://huggingface.co/Avinashricky211/AViGPT
-cd AViGPT
-pip install torch transformers
+git clone https://github.com/Avinashricky211/AviGPT.git
+cd AviGPT
+pip install -r requirements.txt
+streamlit run app.py
 ```
 
+Or programmatically in Python:
+
 ```python
 import torch
 from transformers import GPT2LMHeadModel, GPT2TokenizerFast
 from autonomous_bus import AutonomousHardwareBus
 
-model_id = "Avinashricky211/AViGPT"
+model_id = "AvinashRicky/AViGPT"
 tokenizer = GPT2TokenizerFast.from_pretrained(model_id)
 model = GPT2LMHeadModel.from_pretrained(model_id).to("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -186,11 +210,12 @@ print(f"Total Response Latency: {metrics['total_latency_s']:.2f} s")
 ## Citation
 
 ```bibtex
-@article{yadlapalli2026avigpt,
+@article{Avinash2026avigpt,
   title={AViGPT: Decoupling Neural Reasoning from Parametric Memory via a Sub-Millisecond Native NVMe Hardware Bus},
-  author={Yadlapalli, Avinash Ricky},
+  author={Avinash Ricky Yadlapalli},
   year={2026},
   journal={AViGPT Technical Report},
-  howpublished={\url{https://huggingface.co/Avinashricky211/AViGPT}}
+  howpublished={\url{https://huggingface.co/AvinashRicky/AViGPT}},
+  url={https://github.com/Avinashricky211/AviGPT}
 }
 ```