---

language:

• en
• zh

license: apache-2.0

tags:

• dynamic-subspace-engine
• qwen3.8
• nvfp4
• 4bit
• compressed-tensors
• low-vram
• wddm-shared-memory
• text-generation

pipeline_tag: text-generation

inference: false

---

Qwen3.8-27B-NVFP4 with Dynamic Subspace Engine (DSE)

!Dynamic Subspace Engine Performance

This repository contains the production Dynamic Subspace Engine (DSE) runner and configuration for Qwen3.8-27B-NVFP4 (4-bit Compressed Tensors), enabling full 27-Billion parameter reasoning and code generation on consumer GPUs such as the NVIDIA GeForce RTX 5070 (12GB GDDR7).

---

⚡ Key Highlights

• Hardware Footprint: Runs the 27B model across 12GB VRAM + 32GB System RAM using WDDM Shared Memory & PCIe direct streaming.
• Upfront Subspace Predictor ($P_{\text{pred}}$): Prunes 95%–98% of SwiGLU MLP computations on the fly without loss of reasoning coherence.
• Continuous Subspace Softmax ($P_{\text{sub}}$): Filters the 248,077 subword vocabulary down to candidate manifolds, eliminating 910× output logit memory traffic.
• Native Tokenizer: Full support for Qwen3.8 BPE vocabulary (248k tokens) and ChatML formatting (<|im_start|>).
• Dual API Server: Built-in OpenAI (/v1/chat/completions) and Ollama (/api/generate, /api/chat) REST server.

---

🚀 Quickstart

1. Requirements

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
pip install transformers compressed-tensors accelerate fastapi uvicorn matplotlib

2. Interactive Terminal Chat

python run_dse_engine.py --interactive

3. Run a One-Off Prompt

python run_dse_engine.py --prompt "Write a Python script to calculate the golden ratio." --sparsity 0.95

4. Launch the Dual OpenAI & Ollama REST API Server

python run_dse_engine.py --serve --port 8000

---

📄 Technical Reference

For the complete mathematical formulation, error bound derivations, and Single-CCD L3 cache residency theorems, see DYNAMIC_SUBSPACES_RESEARCH_PAPER.md.