Update readme

README.md

@@ -1,435 +1,12 @@
-<p align="center">
-  <img src="assets/apohara-contextforge-logo.png" alt="Apohara · ContextForge" width="460">
-</p>
-
-<h1 align="center">APOHARA · ContextForge</h1>
-
-<p align="center">
-  <strong>The shared-context compiler for multi-agent LLM pipelines.</strong><br>
-  Silicon-native KV cache coordination for AMD Instinct MI300X.
-</p>
-
-<p align="center">
-  <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.11%2B-2B5DF2.svg" alt="Python 3.11+"></a>
-  <a href="LICENSE"><img src="https://img.shields.io/badge/license-Apache%202.0-2ECC71.svg" alt="License Apache 2.0"></a>
-  <a href="https://rocm.docs.amd.com/"><img src="https://img.shields.io/badge/ROCm-7.x-FF6B00.svg" alt="ROCm 7.x"></a>
-  <a href="https://lablab.ai/event/amd-hackathon"><img src="https://img.shields.io/badge/AMD-Hackathon-ED1C24.svg" alt="AMD Hackathon"></a>
-  <a href="#-research-foundation"><img src="https://img.shields.io/badge/papers-10%20implemented-9B59B6.svg" alt="10 Papers"></a>
-  <a href="#-benchmark-results"><img src="https://img.shields.io/badge/benchmark-15%2F15%20PASS-27AE60.svg" alt="V6.0 15/15 PASS"></a>
-  <a href="#-verification"><img src="https://img.shields.io/badge/tests-310%20passed%20%C2%B7%200%20failed-27AE60.svg" alt="310 tests passing"></a>
-  <a href="https://youtu.be/swEcn-6pAmA"><img src="https://img.shields.io/badge/%E2%96%B6%EF%B8%8F-watch%20demo%20on%20YouTube-FF0000.svg" alt="Watch the demo on YouTube"></a>
-</p>
-
-<p align="center">
-  <a href="#-the-problem">Problem</a> ·
-  <a href="#-the-solution">Solution</a> ·
-  <a href="#%EF%B8%8F-demo-video"><b>▶️ Demo video</b></a> ·
-  <a href="#-benchmark-results">Benchmarks</a> ·
-  <a href="#-architecture">Architecture</a> ·
-  <a href="#-quick-start">Quick Start</a> ·
-  <a href="#-research-foundation">Research</a> ·
-  <a href="#-business-value">Business Value</a>
-</p>
-
----
-
-## ⚡ The Problem
-
-In a 5-agent pipeline — **Retriever → Reranker → Summarizer → Critic → Responder** — every agent independently materializes identical KV-cache entries for the shared context (system prompt, user query, retrieved documents). On a 35B MoE model with 192 GB HBM3, this redundancy wastes **40–60 % of VRAM** before a single output token is generated.
-
-```text
-WITHOUT ContextForge (VRAM duplication per agent):
-  Agent 1 (Retriever)   → [KV: system + query + docs]  12 GB
-  Agent 2 (Reranker)    → [KV: system + query + docs]  12 GB  ← DUPLICATE
-  Agent 3 (Summarizer)  → [KV: system + query + docs]  12 GB  ← DUPLICATE
-  Agent 4 (Critic)      → [KV: system + query + docs]  12 GB  ← DUPLICATE
-  Agent 5 (Responder)   → [KV: system + query + docs]  12 GB  ← DUPLICATE
-  ──────────────────────────────────────────────────────────────────
-  Total KV VRAM:           60 GB for context that should need 12 GB
-```
-
-ContextForge intercepts at the vLLM ATOM plugin level — zero model changes, zero latency overhead, shared PagedAttention blocks before materialization.
-
----
-
-## 🧠 The Solution
-
-ContextForge coordinates KV-block sharing across all agents through **10 peer-reviewed mechanisms**, intercepting KV-cache operations at the vLLM V1 ATOM plugin interface. Before any agent materializes a KV block, ContextForge checks whether an identical or semantically equivalent block already exists in the shared registry — and a JCR Safety Gate (V6.0) decides when reuse would corrupt judge-type agents, falling back to dense prefill.
-
-Every optimization traces back to a peer-reviewed paper published at **NeurIPS, ICML, ACL, IJCAI, or arXiv 2026**.
-
-<p align="center">
-  <img src="assets/systems-diagram.jpeg" alt="ContextForge — shared KV via ATOM plugin" width="760">
-</p>
-
-### The 10 Mechanisms
-
-| # | Mechanism | Source | What it does |
-|---|-----------|--------|-------------|
-| 1 | **KVCOMM** | NeurIPS 2025 · [arXiv:2510.12872](https://arxiv.org/abs/2510.12872) | SimHash anchor matching for cross-context offset hints — zero RoPE drift |
-| 2 | **KVFlow** | NeurIPS 2025 | Workflow-step graph eviction — evict agents farthest from execution first |
-| 3 | **PBKV** | May 2026 | 2nd-order Markov predictor — 1.26× faster than KVFlow |
-| 4 | **SemShareKV** | ACL Findings 2025 | LSH + FAISS semantic dedup on Qwen3-Embed-0.6B ONNX |
-| 5 | **RotateKV** | IJCAI 2025 · [arXiv:2501.16383](https://arxiv.org/abs/2501.16383) | Pre-RoPE INT4 quantization — 3.97× VRAM reduction, attention-sink protected |
-| 6 | **CLA + LCKV** | NeurIPS 2024 + NAACL 2025 | Cross-layer upper-KV sharing — 50 % savings on upper layers |
-| 7 | **Queueing Theory** | ICML 2026 | λ_critical stability model — replaces 5 empirical thresholds with rigorous math |
-| 8 | **VisualKVCache** | Feb 2026 | SHA-256 content-hash for images — +44.9 % throughput at 1024 px |
-| 9 | **TokenDance** *(V6)* | Apr 2026 · [arXiv:2604.03143](https://arxiv.org/abs/2604.03143) | Master-Mirror diff storage — **10–17× KV compression** for committee inference |
-| 10 | **JCR Safety Gate** *(V6)* | Jan 2026 · [arXiv:2601.08343](https://arxiv.org/abs/2601.08343) | INV-15: Critic agent dense prefill when JCR risk > 0.7 |
-
-**Built on AMD-native stack:** ROCm 7.x · AITER · PyRSMI · ATOM plugin · HIP · vLLM V1 · LMCache · AMD DevCloud MI300X.
-
----
-
-## 🎬 Live Demo
-
-Real metrics from `demo/app.py` running against the full ContextForge stack — five agents, real Qwen3 tokenizer, real LSH+FAISS dedup, INV-15 enforced live. Side-by-side comparison: **263 → 53 tokens, 79.85 % savings** with ContextForge; passthrough on the right.
-
-### ▶️ Demo video
-
-<p align="center">
-  <a href="https://youtu.be/swEcn-6pAmA" title="Watch the ContextForge demo on YouTube">
-    <img src="https://img.youtube.com/vi/swEcn-6pAmA/maxresdefault.jpg"
-         alt="▶️ Watch the ContextForge demo on YouTube — 79.85% token savings, INV-15 firing on the Critic"
-         width="780">
-  </a>
-</p>
-
-<p align="center">
-  <a href="https://youtu.be/swEcn-6pAmA"><b>▶️ Watch on YouTube</b></a>
-  &nbsp;·&nbsp;
-  <a href="https://github.com/SuarezPM/Apohara_Context_Forge/raw/main/assets/video_live.mp4">Download raw mp4 (5.2 MB)</a>
-  <br>
-  <em>End-to-end run: query → 5-agent pipeline → 79.85 % token savings → JCR Safety Gate fires INV-15 on the Critic.</em>
-</p>
-
-### Static frame
-
-<p align="center">
-  <img src="assets/screenshots/dashboard_live_demo.png" alt="Live Demo — With vs Without ContextForge, 79.85% savings, INV-15 firing on the Critic" width="960"><br>
-  <em>Live Demo tab — left: query input. Right: <b>With ContextForge</b> (79.85 % savings, INV-15 fires on the Critic) vs. <b>Without ContextForge</b> (passthrough, 0 % savings).</em>
-</p>
-
-```
-[ContextForge Enabled] Processed: What is machine learning and how does it work?
-
-agents: 5
-tokens_before: 263
-tokens_after: 53
-avg_ttft_ms: 23.78
-token_savings_pct: 79.85%
-dedup_rate_pct:    79.85%
-registry_size: 4
-vram_mode: relaxed
-strategy: register+lsh+faiss
-
-[JCR Safety Gate / INV-15]
-  critic risk: 1.000
-  critic dense_prefill: True
-  reason: INV-15: judge role='critic' risk=1.00 > threshold=0.70 → dense prefill mandated
-```
-
-### V6 Live Snapshot — TokenDance + JCR Safety Gate
-
-<p align="center">
-  <img src="assets/screenshots/dashboard_v6_snapshot.png" alt="Architecture tab — TokenDance Master-Mirror + JCR Safety Gate live snapshots" width="640"><br>
-  <em>Architecture tab — <b>TokenDance Master-Mirror Storage</b> (5-agent demo, 4.71× compression) and <b>JCR Safety Gate</b> firing INV-15 (risk = 1.000, dense_prefill = True).</em>
-</p>
-
-<p align="center">
-  <img src="assets/screenshots/dashboard_aiter_config.png" alt="AITER ROCm Config — MI300X" width="520"><br>
-  <em>AITER ROCm Config (MI300X) — <code>rocm_available: True</code>, 7 documented env vars, AMD-published speedups: 3× fused MoE, 2× block-scale GEMM, 2-4× FP8 memory.</em>
-</p>
-
----
-
-## 🏗️ Architecture
-
-```mermaid
-flowchart TB
-    subgraph Agents["5-Agent Pipeline"]
-        A1[Retriever]
-        A2[Reranker]
-        A3[Summarizer]
-        A4[Critic]
-        A5[Responder]
-    end
-
-    subgraph CF["ContextForge MCP Server · FastAPI + asyncio"]
-        direction TB
-        REG["Context Registry<br/>register · clear · get_shared_context"]
-        LSH["LSH Token Matcher<br/>SimHash · block-aligned"]
-        FAISS["FAISS ANN Index<br/>O(log n) cosine search"]
-        VRAM["VRAM-Aware Cache<br/>5-mode pressure eviction"]
-        TD["TokenDance Storage<br/>Master + N-1 sparse diffs"]
-        JCR{"JCR Safety Gate<br/>INV-15"}
-        COORD["Compression Coordinator<br/>LLMLingua-2 + APC"]
-    end
-
-    subgraph Serving["AMD MI300X · ROCm 7.x"]
-        VLLM["vLLM V1 + ATOM plugin<br/>--enable-prefix-caching"]
-        AITER["AITER kernels<br/>fused MoE · MHA · GEMM"]
-        HBM[("192 GB HBM3<br/>Qwen3.6-35B-A3B MoE")]
-    end
-
-    A1 & A2 & A3 & A4 & A5 -->|register context| REG
-    REG --> LSH --> FAISS --> VRAM
-    REG --> TD
-    A4 --> JCR
-    JCR -->|risk > 0.7| VLLM
-    JCR -->|risk ≤ 0.7| COORD
-    REG --> COORD
-    COORD --> VLLM
-    VLLM --> AITER --> HBM
-
-    style JCR fill:#FF6B00,stroke:#fff,color:#fff
-    style TD fill:#FF6B00,stroke:#fff,color:#fff
-    style AITER fill:#ED1C24,stroke:#fff,color:#fff
-    style HBM fill:#ED1C24,stroke:#fff,color:#fff
-```
-
----
-
-## 📊 Benchmark Results
-
-> ✅ **Validated on AMD Instinct MI300X (192 GB HBM3) — AMD DevCloud ATL1 · 2026-05-10**
-
-### V6.0 Benchmark — 15 / 15 PASS
-
-| #  | Scenario | Time (ms) | Throughput (tok/s) | VRAM (GB) | Result |
-|----|----------|-----------|--------------------|-----------|--------|
-| 1  | anchor_pool_resolution            |   2.87 |   173,986 | 0.10 | ✅ PASS |
-| 2  | cla_metadata_layer                |   0.28 | 5,620,918 | 0.05 | ✅ PASS |
-| 3  | rotate_kv_quantization            |  21.70 | 1,510,156 | 0.20 | ✅ PASS |
-| 4  | step_graph_execution              |   0.37 |   268,906 | 0.30 | ✅ PASS |
-| 5  | kv_aware_routing                  |   0.04 |   269,251 | 0.10 | ✅ PASS |
-| 6  | lmcache_bridge_save_load          |   0.03 | 3,752,204 | 0.05 | ✅ PASS |
-| 7  | atom_plugin_hooks                 |   0.11 | 6,961,486 | 0.10 | ✅ PASS |
-| 8  | pbkv_prediction                   |   0.12 |   581,207 | 0.05 | ✅ PASS |
-| 9  | workflow_aware_eviction           |   0.02 | 6,127,076 | 0.10 | ✅ PASS |
-| 10 | embedding_engine_encoding         | 268.86 |    20,457 | 0.10 | ✅ PASS |
-| 11 | **queueing_controller_stability** | 250.00 |     4,000 | 0.15 | ✅ **PASS** |
-| 12 | **visual_kvcache_cross_agent**    | 150.00 |   177,633 | 0.01 | ✅ **PASS** |
-| 13 | **speculative_coordinator_speedup** | 100.00 |        80 | 0.05 | ✅ **PASS** |
-| 14 | **token_dance_compression** *(V6)*    | 120.00 |    20,000 | 0.00 | ✅ **PASS** |
-| 15 | **jcr_gate_critic_safety** *(V6)*     |   5.00 |     1,800 | 0.00 | ✅ **PASS** |
-
-### V6.0 Key Targets — 8 / 8 PASS
-
-| Metric | Result | Target | Status |
-|--------|--------|--------|--------|
-| QueueingController λ_critical deviation | **0.00 %** | < 10 % | ✅ |
-| VisualKVCache encoder-call reduction | **5.0 ×** | ≥ 4 × | ✅ |
-| Speculative acceptance rate | **≥ 0.875** | > 0.70 | ✅ |
-| Speculative speedup | **5.59–8.00 ×** | > 2 × | ✅ |
-| TokenDance compression ratio | **10.81 ×** | ≥ 10 × | ✅ |
-| TokenDance reconstruction error | **1.19 × 10⁻⁷** | ≤ 1 × 10⁻⁴ | ✅ |
-| JCR INV-15 violations | **0** | 0 | ✅ |
-| JCR Critic dense rate (high-risk sweep) | **1.000** | ≥ 0.5 | ✅ |
-
-<p align="center">
-  <img src="assets/screenshots/benchmark_v6_terminal.png" alt="V6.0 benchmark terminal output — S-14 token_dance 10.81x, S-15 jcr_gate 0 violations" width="780"><br>
-  <em>Live terminal output of <code>python demo/benchmark_v5.py</code> — S-14 TokenDance <b>10.81×</b> compression with reconstruction error <b>1.19e-07</b>, S-15 JCR Safety Gate <b>0 INV-15 violations</b>.</em>
-</p>
-
----
-
-## 📈 Key Stats
-
-| Metric | Value |
-|--------|-------|
-| Live token savings (5-agent demo) | **79.85 %** |
-| Multi-agent VRAM reduction | **68 %** |
-| TTFT improvement | **7.8 ×** |
-| TokenDance compression (12-agent committee) | **10.81 ×** |
-| JCR Safety Gate INV-15 violations | **0** |
-| Tests passing | **310 / 310** *(0 failed · 23 skipped)* |
-| Benchmark scenarios | **15 / 15 PASS** |
-| Peer-reviewed papers implemented | **10** |
-| System invariants enforced | **15** |
-
-<p align="center">
-  <img src="assets/screenshots/dashboard_key_stats.png" alt="Architecture tab — Key Statistics panel" width="520"><br>
-  <em>Key Statistics panel rendered live in the dashboard's Architecture tab.</em>
-</p>
-
----
-
-## 🚀 Quick Start
-
-### Prerequisites
-
-- Python 3.11 +
-- AMD GPU with ROCm 7.x **or** any CPU box for hermetic dev
-- 16 GB RAM minimum (192 GB HBM3 recommended for full vLLM run)
-
-### Install
-
-```bash
-git clone https://github.com/SuarezPM/Apohara_Context_Forge.git
-cd Apohara_Context_Forge
-pip install -e .
-```
-
-### Run the benchmark
-
-```bash
-python demo/benchmark_v5.py
-# → 15/15 PASS · all 8 V5+V6 targets PASS
-```
-
-### Launch the dashboard
-
-```bash
-python demo/app.py
-# Open http://localhost:7860
-```
-
-Four tabs: **Live Demo** · **Real-time Metrics** · **Benchmark Results** · **Architecture**
-
-### Run the test suite
-
-```bash
-PYTHONPATH=. pytest tests/ -q
-# → 310 passed · 23 skipped · 0 failed
-```
-
----
-
-## 🔬 Research Foundation
-
-ContextForge implements **six 2025–2026 papers** as production code, plus four established baselines. Every numeric claim in this README is backed by a peer-reviewed result.
-
-| Paper | Venue · Year | Module | Validated metric |
-|-------|--------------|--------|------------------|
-| KVCOMM · [arXiv:2510.12872](https://arxiv.org/abs/2510.12872) | NeurIPS 2025 | `kv_offset/anchor_pool.py` | 7.8× TTFT improvement |
-| RotateKV · [arXiv:2501.16383](https://arxiv.org/abs/2501.16383) | IJCAI 2025 | `quantization/rotate_kv.py` | 3.97× VRAM reduction at INT4 |
-| Cross-Attention Speculative · [arXiv:2505.24544](https://arxiv.org/abs/2505.24544) | May 2026 | `decoding/speculative_coordinator.py` | 5.59–8 × decode speedup |
-| Queueing-aware vLLM · ICML 2026 | ICML 2026 | `scheduling/queueing_controller.py` | 0.00 % λ_critical deviation |
-| **TokenDance** · [arXiv:2604.03143](https://arxiv.org/abs/2604.03143) | Apr 2026 | `storage/token_dance.py` | 10.81× compression, 1.19e-7 error |
-| **JCR Failure Mode** · [arXiv:2601.08343](https://arxiv.org/abs/2601.08343) | Jan 2026 | `safety/jcr_gate.py` | INV-15 — 0 violations across sweep |
-| LLMLingua-2 | ACL 2024 | `compression/compressor.py` | 8× memory reduction |
-| CLA + LCKV | NeurIPS 2024 + NAACL 2025 | `kv_offset/cla_metadata.py` | 50 % upper-layer KV savings |
-| VisualKVCache | Feb 2026 | `multimodal/visual_kv_cache.py` | 5.0× encoder-call reduction |
-| vLLM ATOM plugin (production) | vLLM 0.9.x | `serving/atom_plugin.py` | Native V1 KV interception |
-
----
-
-## 🟥 Why AMD Instinct MI300X
-
-ContextForge is **silicon-native** for the MI300X — not a port of CUDA code, not a generic "ROCm-compatible" wrapper.
-
-| Layer | What we use | Why MI300X |
-|-------|-------------|------------|
-| **HBM** | 192 GB HBM3 (single-GPU 35B MoE) | Fits Qwen3.6-235B-A22B without tensor-parallelism overhead |
-| **Compute** | AITER fused MoE + MHA kernels | **3× faster MoE**, **2× block-scale GEMM**, FP8 2-4× memory |
-| **Telemetry** | PyRSMI / `/sys/class/drm` | Real-time VRAM pressure for the 5-mode eviction policy |
-| **Networking** | RCCL · `NCCL_MIN_NCHANNELS=112` | Multi-GPU collective KV sharing (TokenDance All-Gather) |
-| **Plugin surface** | vLLM V1 ATOM (`vllm.general_plugins`) | Zero model code change — intercept BEFORE block materialization |
-| **Stability flag** | `AITER_ENABLE_VSKIP=0` | Hard-coded by [`AITERConfig`](apohara_context_forge/serving/aiter_config.py) — prevents documented kernel crashes |
-
-> **Validated on AMD DevCloud ATL1.** All 15 benchmark scenarios run on real MI300X hardware with ROCm 7.x — see `logs/benchmark_v6_final.txt`.
-
----
-
-## 💼 Business Value
-
-### TAM / SAM / SOM
-
-| Tier | Definition | 2027 estimate |
-|------|------------|---------------|
-| **TAM** | Global LLM-inference market (all hardware, all workloads) | **$50 B** |
-| **SAM** | Multi-agent + RAG inference on AMD-class accelerators | **$8 B** |
-| **SOM** *(3-yr)* | Enterprise agentic platforms self-hosting on MI300X / MI325X | **$420 M** |
-
-### Where the value lands
-
-- **40–60 % VRAM saved** per multi-agent workload → **fewer GPUs needed** for the same throughput. On a 192 GB MI300X box, that's $15-25 K of capex unlocked per node.
-- **7.8× TTFT improvement** + 5.59–8 × speculative speedup → response-time SLOs that were previously unreachable on commodity hardware become trivial.
-- **JCR Safety Gate (INV-15)** → the first engineered answer to "when does KV reuse silently break my judge agent?" — a known failure mode that has, until now, blocked KV reuse from production agentic pipelines.
-
-### Revenue streams
-
-1. **Enterprise SaaS** — managed ContextForge MCP servers per tenant, priced per-GPU-hour saved (verifiable via `metrics/snapshot`).
-2. **Self-hosted license** — Apache-2.0 core, paid enterprise tier with SLAs, AITER tuning packs, and audit-grade INV-15 telemetry export.
-3. **AMD partnership / co-marketing** — reference design for MI300X agentic deployments; flagship customer logo for the AMD AI Stack.
-4. **Plugin marketplace** — third-party mechanisms (custom safety gates, vertical-specific routers) that ride the ContextForge MCP interface.
-
-### Who buys it
-
-- **Foundation-model labs** running 5-agent reasoning stacks (debate, critic, planner architectures).
-- **Enterprise RAG vendors** with multi-tenant constraints — every shared system prompt is wasted VRAM today.
-- **Sovereign / on-prem GPU clusters** with AMD MI300X hardware that need a CUDA-free alternative to vLLM-only deployments.
-
----
-
-## ✅ Verification
-
-| Check | Result |
-|-------|--------|
-| `pytest tests/` | **310 passed · 23 skipped · 0 failed** |
-| `python demo/benchmark_v5.py` | **15 / 15 PASS** · all 8 V5+V6 targets PASS |
-| `python demo/app.py` | Gradio 6.x · HTTP 200 on `/` · live 79.85 % savings |
-| Hermetic CI mode | No GPU, no TCP, no model downloads — all deps gated by `try / import` |
-
-System invariants enforced:
-
-| ID | Invariant | Module |
-|----|-----------|--------|
-| INV-10 | RotateKV pre-RoPE only — never quantize post-RoPE tensors | `rotate_kv.py` |
-| INV-11 | QueueingController never evicts below `ceil(λ × E[S] × E[blocks] × 1.15)` | `queueing_controller.py` |
-| INV-12 | SpeculativeCoordinator: target always generates final authoritative token | `speculative_coordinator.py` |
-| INV-13 | VisualKVCache content hash is SHA-256 of raw bytes — never of embeddings | `visual_kv_cache.py` |
-| INV-14 | Dashboard "SIMULATION MODE" banner shown for synthetic data | `app.py`, `dashboard.py` |
-| **INV-15** | **JCR Safety Gate: Critic uses dense prefill when risk > 0.7** | **`safety/jcr_gate.py`** |
-
----
-
-## 🗺️ Roadmap
-
-| Version | Status | Highlights |
-|---------|--------|-----------|
-| V4.0 | ✅ Complete | AnchorPool · EmbeddingEngine ONNX · CLA metadata · RotateKV INT4 · StepGraph · KVAwareRouter · LMCacheBridge · ATOM plugin |
-| V5.0 | ✅ Complete | QueueingController (ICML 2026) · VisualKVCache · SpeculativeCoordinator · Gradio Dashboard |
-| V5.x | ✅ Complete | S-3 4D-indexing fix · S-13 acceptance criterion → 13 / 13 PASS |
-| **V6.0** | ✅ **Complete** | **TokenDance Master-Mirror · JCR Safety Gate (INV-15) · AITER ROCm config → 15 / 15 PASS** |
-| V6.x | 📋 Planned | Multi-node distributed KV via LMCache · HIP custom kernels for RotateKV FWHT · Plugin marketplace SDK |
-
----
-
-## 🛠️ Tech Stack
-
-**Runtime · serving** Python 3.11+ · FastAPI · `Bun.serve()`-style lifespan · Gradio 6.x · Plotly · Pydantic 2 · uvicorn
-
-**Inference · KV** vLLM V1 (ATOM plugin) · LMCache · PyTorch ROCm · ONNX Runtime · transformers · LLMLingua-2
-
-**Index · math** FAISS (CPU + ROCm) · NumPy · SimHash 64-bit · M/G/1 queueing model · SHA-256 content hashing
-
-**AMD-native** ROCm 7.x · AITER (fused MoE / MHA / RMSNorm / GEMM) · PyRSMI · HIP · RCCL · MI300X HBM3
-
----
-
-## 🤝 Contributing & License
-
-- **License:** Apache 2.0 — see [LICENSE](LICENSE).
-- **Issues / PRs:** [github.com/SuarezPM/Apohara_Context_Forge](https://github.com/SuarezPM/Apohara_Context_Forge).
-- **Contact:** Pablo (`p.ms.08@hotmail.com`) · @SuarezPM on GitHub.
-
----
-
-<p align="center">
-  <strong>APOHARA · ContextForge</strong> — built for the AMD AI Hackathon 2026<br>
-  <em>"The pitch is the curve, not a single number."</em>
-</p>
-
-
 ---
 title: ContextForge Demo
 emoji: ⚡
 colorFrom: red
-colorTo: orange
+colorTo: green
 sdk: gradio
 sdk_version: 5.29.0
 app_file: demo/app.py
 pinned: false
----
+---
+
+# ContextForge — Shared Context Compiler for Multi-Agent LLM