File size: 7,491 Bytes
df098b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 | ---
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.*
|