Update README.md

README.md

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----
-license: other
-license_name: codynamics-commercial
-license_link: https://www.codynamicslab.com/license
----
+---
+license: other
+license_name: codynamics-commercial
+license_link: https://www.codynamicslab.com/license
+language:
+- en
+tags:
+- document-question-answering
+- text-generation
+- long-context
+- information-retrieval
+- enterprise-ai
+- latch
+- multi-document-reasoning
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+pipeline_tag: text-generation
+library_name: vllm
+---
+
+# LATCH — Qwen 2.5 14B
+
+**CoDynamics Lab Corporation** | [Website](https://www.codynamicslab.com) | [🛒 Buy Self-Hosted License — $79](https://codynamicslab.gumroad.com/l/latch-qwen14b) | [Request Gated Access](#request-access) | [Contact](mailto:mike@codynamicslab.com)
+
+> ⚠️ **This is a gated repository.** Model weights are available via two paths — see [Deployment Options](#deployment-options) below.
+
+---
+
+## What Is LATCH
+
+**LATCH** is a proprietary inference layer built on top of `Qwen/Qwen2.5-14B-Instruct` that eliminates the long-context performance penalty for document-heavy workloads.
+
+Standard LLMs re-process every document from scratch on every query. LATCH removes this cost entirely — documents are prepared once and subsequent queries run at dramatically reduced latency regardless of document length or count.
+
+**This is not RAG. This is not prompt compression.** It is a fundamentally different approach to long-context inference that operates at the model level.
+
+Architectural details are proprietary.
+
+---
+
+## Performance Results
+
+All benchmarks run on **NVIDIA A100 80GB** with vLLM serving infrastructure.
+
+### Speed
+
+| Metric | Baseline (Qwen 2.5 14B) | LATCH | Improvement |
+|---|---|---|---|
+| **Time-To-First-Token (cold)** | 23.1s | **0.11s** | **210× faster** |
+| **TTFT Speedup (avg, customer pack)** | 4.47s | 0.11s | **42.9×** |
+| **End-to-End Query Speedup** | 6.55s | 2.02s | **5.2×** |
+| **Cache Reload Time** | 23.1s | **0.0016s** | **246× faster** |
+
+### Quality — Customer Document Pack
+
+| Benchmark Category | Baseline | LATCH | Delta |
+|---|---|---|---|
+| Cross-Document Comparison | 41.5% | **49.4%** | +7.9pp |
+| Cross-Document Format | 40.5% | **68.8%** | +28.3pp |
+| Cross-Document Retrieval | 40.4% | **48.1%** | +7.7pp |
+| Selective Retrieval | 35.2% | **47.2%** | +12.0pp |
+| **Overall Mean token-F1** | **39.4%** | **53.4%** | **+14.0pp** |
+
+### Benchmark Gates
+
+| Gate | Result |
+|---|---|
+| Single-Document Gate | 11/12 ✅ |
+| Multi-Document Gate | 11/12 ✅ |
+| 256K Memory Sweep | Passing |
+
+> **Multi-doc pass rate: 91.7%** — the highest of any model family in the current LATCH portfolio.
+
+---
+
+## How It Works
+
+LATCH intercepts the standard inference path and replaces the costly per-query document processing step with a persistent representation that is prepared once and reused across all subsequent queries against the same document set.
+
+The result is a response that begins in under 120 milliseconds — before the user has practically finished pressing Enter — regardless of how many documents are in the corpus.
+
+The underlying method is proprietary and patent pending. CoDynamics Lab does not publish architectural details.
+
+---
+
+## Hardware Requirements
+
+| Component | Minimum | Recommended |
+|---|---|---|
+| GPU | NVIDIA A100 40GB | NVIDIA A100 80GB |
+| VRAM | ~30 GB | 80 GB |
+| CPU RAM | 64 GB | 128 GB |
+| Storage | 50 GB | 100 GB |
+| Inference Runtime | vLLM | vLLM ≥ 0.4 |
+
+> LATCH reduces peak VRAM consumption by approximately **50%** versus standard Qwen 2.5 14B serving, enabling more concurrent instances per node.
+
+---
+
+## Deployment Options
+
+### 🔒 Option 1: Self-Hosted License — $79
+
+Run LATCH on your own A100 or H100. Your documents never leave your infrastructure.
+
+**[Buy now at codynamicslab.gumroad.com](https://codynamicslab.gumroad.com/l/latch-qwen14b)**
+
+Upon purchase you receive:
+- Private registry pull token for the LATCH Docker image
+- License key (validated at container startup)
+- One-line deployment command
+- Access to future runtime updates
+
+```bash
+LICENSE_KEY=xxxx-xxxx docker compose pull && docker compose up -d
+```
+
+Compatible with standard OpenAI-format API clients.
+
+---
+
+### ☁️ Option 2: Managed Hosted Instance — Coming Soon
+
+Spin up a LATCH-ready GPU instance directly from CoDynamics Lab. No infrastructure setup required.
+
+- Pay by the hour — billed by wall-clock second
+- Includes batch JSON query interface
+- Upload documents, submit a structured prompt list, export results with full telemetry
+- Every session outputs side-by-side cost savings vs. standard Qwen baseline
+
+**[Join the waitlist](mailto:mike@codynamicslab.com?subject=LATCH%20Managed%20Instance%20Waitlist)**
+
+---
+
+### 🔑 Option 3: Gated Repository Access (Research / Enterprise)
+
+Request direct access for evaluation, research, or enterprise licensing discussions.
+
+---
+
+## Intended Use
+
+**Primary use cases:**
+- M&A and private equity due diligence (multi-document data room analysis)
+- Legal document review and cross-contract comparison
+- Compliance and regulatory document monitoring
+- Financial research and filing analysis
+- Any high-volume, repeated-query workload against a fixed document corpus
+
+**Out of scope:**
+- Real-time web search or retrieval-augmented generation
+- General-purpose conversational AI without a document corpus
+- Consumer applications
+
+---
+
+## Limitations & Known Weaknesses
+
+- **Short-context standard QA:** LATCH is optimized for long-context, multi-document workloads. It does not improve performance on standard short-context QA benchmarks.
+- **Document pre-preparation required:** Documents must be prepared before querying. This is a one-time cost per document set that is fully amortized across subsequent queries.
+- **Cross-document retrieval is the weakest benchmark slice:** Document-selection tasks with heavy distractors are the most challenging workload category.
+
+---
+
+## Request Access
+
+**Three ways to get started:**
+
+| Path | Best for | Action |
+|---|---|---|
+| **Self-hosted license** | Teams with their own A100/H100 who need full data privacy | [Buy on Gumroad — $79](https://codynamicslab.gumroad.com/l/latch-qwen14b) |
+| **Managed hosted instance** | Teams who want zero infrastructure setup | [Join waitlist](mailto:mike@codynamicslab.com?subject=LATCH%20Managed%20Instance%20Waitlist) |
+| **Gated repo access** | Research, enterprise evaluation, volume licensing | Click Request Access above |
+
+For gated access requests:
+1. Click the **Request Access** button above
+2. Briefly describe your use case and organization
+3. Our team will review and respond within 2 business days
+
+📧 [mike@codynamicslab.com](mailto:mike@codynamicslab.com)  
+🌐 [www.codynamicslab.com](https://www.codynamicslab.com)
+
+---
+
+## License
+
+This model is released under the **CoDynamics Commercial License**.
+- Purchase includes a single-instance deployment license
+- Commercial or production use beyond the licensed instance requires a separate agreement
+- Redistribution of model weights is strictly prohibited
+
+See [LICENSE](https://www.codynamicslab.com/license) for full terms.
+
+---
+
+## Citation
+
+If you cite LATCH benchmark results in research, please use:
+
+```bibtex
+@misc{codynamics2026latch,
+  title        = {LATCH: Proprietary Long-Context Inference Layer},
+  author       = {CoDynamics Lab Corporation},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/CoDynamicsLab/LATCH-Qwen2.5-14B}},
+  note         = {Patent Pending. Architectural details proprietary.}
+}
+```
+
+---
+
+*CoDynamics Lab Corporation — Eliminating the Long-Context Tax.*