--- 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.*