| --- |
| 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.* |
|
|
