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enver
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Model Details
Model Provider
enver
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
Stage 15.2)In al-Jurjānī's Dalāʾil al-Iʿjāz, deep relational deduction (ترتيب المعاني في النفس — Tartīb al-Maʿānī fī al-Nafs) occurs when comprehending the Arabic clause and its syntactic dependencies (Taʿalluq), whereas uttering the target words (حذاء الألفاظ — Ḥidhāʾ al-Alfāẓ) follows the already-constructed semantic blueprint:
seq_len > 16): Executes all 64 layers (100% of Qwen3.8-27B) together with the 12-Slot Stage 13.2 Books Mastery Bridge (scale = 0.65) + 6-Pillar GSCA MoE + Sabk Anaphora so the KV/recurrent cache holds the exact 64-layer semantic state Zmaʿnaˉ.seq_len <= 16): Elides K∈{4,8,12} upper linear-attention blocks and replaces each with a lightweight 64-rank HidhaAlAlfazResidualAdapter (120 MB total across 24 layers) conditioned on a cached Semantic Hub Summary (z8,z12) from Layers 31 and 47.SibawayhSlicedLMHead): Preserves 100% of the active English & Arabic tokens (148,891 tokens, 0.000000 logit divergence) while pruning 99,429 unused CJK tokens (-1.95 GB VRAM saved).الحروف الأصلية العربية): Technical parenthetical anchors are always emitted in original Arabic script ((الصانع), (حادث), (ترجيح بلا مرجح), (تمهيد), (ضرورة), (قياس)), never Latin transliterations.causal_conv1d Kernel: Includes a zero-compile Triton causal_conv1d.py drop-in module so Qwen3.8-27B's GatedDeltaNet layers run at full hardware speed without C++ wheel compilation errors or slow fallback warnings.Qwen3.8-27B 4-Bit NF4 on NVIDIA A40)| Decode Mode | Active Decode Depth | Mean Speed (tok/s) | Peak Speed (tok/s) | Word Throughput (words/s) | Total Time (10 Gold Passages) | Wall-Clock Speedup | Classical Gold chrF++ | Classical Gold BLEU | Modern MSA-12 chrF++ |
|---|---|---|---|---|---|---|---|---|---|
Pure Autoregressive Baseline (K=0) | 64L / 64L | 11.03 tok/s | 11.24 tok/s | 8.59 words/s | 124.03s | 1.00x | 60.102 | 32.52 | 92.090 |
Stage 15.2 Ultra-Fidelity (--ultra-fidelity, K=4) | 64L Prefill / 60L Decode | 28.82 tok/s | 52.87 tok/s | 22.49 words/s | 50.02s | 2.48x | 60.599 | 33.67 | 92.090 |
Stage 15.2 Balanced Default (--27b, K=8) | 64L Prefill / 56L Decode | 30.36 tok/s | 38.61 tok/s | 23.53 words/s | 43.77s | 2.83x | 59.734 | 32.86 | 92.090 (81.85 BLEU) |
Stage 15.2 Turbo Mode (--turbo, K=12) | 64L Prefill / 52L Decode | 34.57 tok/s | 49.50 tok/s | 26.90 words/s | 37.32s | 3.32x | 58.822 | 33.39 | 92.090 |
O(1) latency) plus algorithmic fallback.25/25):
محدث (muḥdith [Originator] vs. muḥdath [originated]), مؤثر (muʾaththir [Effective Cause] vs. muʾaththar [effected]), and مرجح (murajjiḥ [Determiner] vs. murajjaḥ [preponderated]) via Sībawayhian governance and theological constraints (100.0% accuracy).100.0% passive voice accuracy).0.00% Trap Leakage):
92.09 chrF++ on MSA-12; 0.00% modern drift on classical polysemous terms like العالم, الدور, الشارع, الفاعل, الحال, العين, القدر, المحل).100% on 8/8 Multi-Clause Chains):
ـه, ـها, ـهم) back to their exact antecedents across long multi-sentence passages.bash
git clone https://huggingface.co/enver/rootformers_stage_v15.2cd rootformers_stage_v15.2chmod +x install.sh run_standalone_server.sh./install.sh./run_standalone_server.shOr download with huggingface_hub (automatically tracked by Hugging Face download metrics):
python
from huggingface_hub import snapshot_download local_dir = snapshot_download(repo_id="enver/rootformers_stage_v15.2")print("Downloaded standalone release to:", local_dir)Then open http://localhost:17800 in your desktop or mobile browser.
./run_standalone_server.sh)| Command | Hardware / Backbone Loaded | RAM / VRAM | Description |
|---|---|---|---|
./run_standalone_server.sh | Auto (27B 4-Bit K=8 Naẓm on GPU / 1.7B on CPU) | ~14.2 GB VRAM (27B) or ~4.5 GB RAM (CPU) | Automatically loads Stage 15.2 Qwen3.8-27B 4-Bit NF4 + Asymmetric Naẓm (K=8, 30.36 tok/s, 2.83x speedup) on CUDA GPUs, or Qwen3-1.7B Sovereign (3.3 GB) + BPE-Free Bridge on CPU. |
./run_standalone_server.sh --ultra-fidelity | Qwen3.8-27B 4-Bit NF4 (K=4 Naẓm, 64L→60L) | ~14.2 GB VRAM | Highest Classical Gold fidelity (60.599 chrF++ / 33.67 BLEU) at 28.82 tok/s mean (52.87 tok/s peak, 2.48x speedup). |
./run_standalone_server.sh --27b | Qwen3.8-27B 4-Bit NF4 (K=8 Naẓm, 64L→56L) | ~14.2 GB VRAM | Balanced default: runs all 64 layers + 12-Slot Bridge on Arabic prefill and 56 layers + Ḥidhāʾ al-Alfāẓ Residual Adapters on English BPE decode (30.36 tok/s, 23.53 words/sec, 59.734 chrF++, 92.09 chrF++ on MSA-12). |
./run_standalone_server.sh --turbo | Qwen3.8-27B 4-Bit NF4 (K=12 Naẓm, 64L→52L) | ~14.2 GB VRAM | Turbo mode: 34.57 tok/s mean (49.50 tok/s peak, 26.90 words/sec, 33.39 BLEU, 3.32x speedup). |
./run_standalone_server.sh --8gb | Strict 8GB VRAM Mode (27B 4-Bit) | < 7.2 GB Peak VRAM | Runs the 27B model on 8 GB consumer GPUs (RTX 3070/4060) using Dual-Stream Pinned-Memory Asynchronous Layer Prefetch (Taqdīm al-ʿĀmil). |
./run_standalone_server.sh --1.7b | Force Qwen3-1.7B Sovereign (GPU or CPU) | ~4.0 GB VRAM/RAM | Loads the bundled 3.3 GB 1.7B safetensors + 12-Layer BPE-Free Foundation + Tri-Layer Bridge. |
./run_standalone_server.sh --cpu | Force CPU Full Neural (1.7B) | ~4.5 GB System RAM | Runs full neural translation + 6-tab workbench 100% on CPU (zero GPU required). |
./run_standalone_server.sh --fast-cpu | Instant Light CPU (<5 ms) | < 800 MB RAM | Runs the 12-Layer BPE-Free Foundation Model + Khalīlian Root/Wazn Analyzer + Sībawayh 16-State Syntax + Ṣarfī Disambiguator in milliseconds on any laptop CPU. |
rootformer_sdk.py)python
from rootformer_sdk import RootformerStage12 # Mode A: Instant BPE-Free Analysis + 12-Layer Foundation Encoding (CPU or GPU, <5 ms)rf = RootformerStage12(device="cpu", load_foundation=True, load_qwen=False)analysis = rf.analyze( "العالم حادث وكل حادث فلا بد له من محدث ومؤثر تام بالذات يرجح وجوده على عدمه، لأن الترجيح بلا مرجح محال في العقل.")print("Rational Triad:", analysis["rational_triad"])print("Sarfi Homographs:", analysis["sarfi_homographs"]) # Mode B: End-to-End Scholarly Translationrf_translator = RootformerStage12(device="cuda", load_foundation=True, load_qwen=True)result = rf_translator.translate( "ندعي أن محدث العالم قادر، لأن العالم فعل محكم مرتب متقن منظوم مشتمل على أنواع من العجائب والآيات")print("English Translation:\n", result["translation"])POST /api/translate)When ./run_standalone_server.sh is running on port 17800:
bash
curl -s http://localhost:17800/api/translate \ -H "Content-Type: application/json" \ -d '{ "text": "العالم حادث وكل حادث فلا بد له من محدث ومؤثر تام بالذات يرجح وجوده على عدمه، لأن الترجيح بلا مرجح محال في العقل.", "mode": "translate" }' | jq .text
rootformers_stage_v15.2/├── config.json # Root Hugging Face model configuration (enables Hub download tracking)├── generation_config.json # Root generation configuration├── tokenizer_config.json # Root tokenizer configuration├── tokenizer.json # Root tokenizer vocabulary & merges├── chat_template.jinja # Root chat template├── model.safetensors # Root Qwen3-1.7B Sovereign safetensors (3.3 GB)├── install.sh # One-click dependency & checkpoint verifier (27B GPU & 1.7B CPU/GPU)├── run_standalone_server.sh # One-click server launcher (--ultra-fidelity, --27b, --turbo, --8gb, --1.7b, --cpu, --fast-cpu)├── server_stage15_2.py # Stage 15.2 unified 27B 4-bit GPU & 1.7B CPU/GPU HTTP server & UI host├── causal_conv1d.py # Native Triton causal_conv1d kernel (zero C++ wheel compilation needed)├── rootformer_sdk.py # Self-contained Python SDK, CLI & Fast-CPU server├── requirements.txt # Minimal Python dependencies├── README.md # Hugging Face Model Card & Quickstart Guide├── aynengine_web_ui_11/ # Responsive 3-Column Desktop IDE & Mobile Web UI (6 Interactive Tabs)├── checkpoints/ # All safetensors & bridge weights included│ ├── qwen3_8_27b_4bit/ # Qwen3.8-27B 4-Bit NF4 safetensors + tokenizer (21 GB, for GPU)│ ├── stage15_2/ # Stage 15.2 Asymmetric Naẓm 24-Layer Residual Adapters (120 MB)│ ├── stage14_5/ # Stage 14.5 Learned Neural BPE-Free-to-BPE Drafter (50 MB)│ ├── stage13_2/ # Stage 13.2 Books Mastery 12-Slot Bridge + GSCA MoE (142 MB)│ ├── stage7_3_mujtahid_sovereign/ # Qwen3-1.7B Sovereign safetensors + tokenizer (3.3 GB, for CPU/GPU)│ └── stage12_bpe_free_foundation_best.pt # 12-Layer (101.55M) BPE-Free Foundation Model (388 MB)├── data/ # 329K Farāhīdī NMFE LUT, 2,469 Lexicon, Qur'an & Consensus Graph├── engine/ # Deterministic Khalīlian, Sībawayhian, Ṣarfī & Sabk engines├── scripts/ # Self-contained Stage 11–15.2 architecture & evaluation modules└── stage15_2/ # Stage 15.2 evaluation suites, training scripts & benchmark logsgit clone https://huggingface.co/enver/rootformers_stage_v15.2cd rootformers_stage_v15.2chmod +x install.sh run_standalone_server.sh./install.sh./run_standalone_server.shfrom huggingface_hub import snapshot_download local_dir = snapshot_download(repo_id="enver/rootformers_stage_v15.2")print("Downloaded standalone release to:", local_dir)from rootformer_sdk import RootformerStage12 # Mode A: Instant BPE-Free Analysis + 12-Layer Foundation Encoding (CPU or GPU, <5 ms)rf = RootformerStage12(device="cpu", load_foundation=True, load_qwen=False)analysis = rf.analyze( "العالم حادث وكل حادث فلا بد له من محدث ومؤثر تام بالذات يرجح وجوده على عدمه، لأن الترجيح بلا مرجح محال في العقل.")print("Rational Triad:", analysis["rational_triad"])print("Sarfi Homographs:", analysis["sarfi_homographs"]) # Mode B: End-to-End Scholarly Translationrf_translator = RootformerStage12(device="cuda", load_foundation=True, load_qwen=True)result = rf_translator.translate( "ندعي أن محدث العالم قادر، لأن العالم فعل محكم مرتب متقن منظوم مشتمل على أنواع من العجائب والآيات")print("English Translation:\n", result["translation"])curl -s http://localhost:17800/api/translate \ -H "Content-Type: application/json" \ -d '{ "text": "العالم حادث وكل حادث فلا بد له من محدث ومؤثر تام بالذات يرجح وجوده على عدمه، لأن الترجيح بلا مرجح محال في العقل.", "mode": "translate" }' | jq .rootformers_stage_v15.2/├── config.json # Root Hugging Face model configuration (enables Hub download tracking)├── generation_config.json # Root generation configuration├── tokenizer_config.json # Root tokenizer configuration├── tokenizer.json # Root tokenizer vocabulary & merges├── chat_template.jinja # Root chat template├── model.safetensors # Root Qwen3-1.7B Sovereign safetensors (3.3 GB)├── install.sh # One-click dependency & checkpoint verifier (27B GPU & 1.7B CPU/GPU)├── run_standalone_server.sh # One-click server launcher (--ultra-fidelity, --27b, --turbo, --8gb, --1.7b, --cpu, --fast-cpu)├── server_stage15_2.py # Stage 15.2 unified 27B 4-bit GPU & 1.7B CPU/GPU HTTP server & UI host├── causal_conv1d.py # Native Triton causal_conv1d kernel (zero C++ wheel compilation needed)├── rootformer_sdk.py # Self-contained Python SDK, CLI & Fast-CPU server├── requirements.txt # Minimal Python dependencies├── README.md # Hugging Face Model Card & Quickstart Guide├── aynengine_web_ui_11/ # Responsive 3-Column Desktop IDE & Mobile Web UI (6 Interactive Tabs)├── checkpoints/ # All safetensors & bridge weights included│ ├── qwen3_8_27b_4bit/ # Qwen3.8-27B 4-Bit NF4 safetensors + tokenizer (21 GB, for GPU)│ ├── stage15_2/ # Stage 15.2 Asymmetric Naẓm 24-Layer Residual Adapters (120 MB)│ ├── stage14_5/ # Stage 14.5 Learned Neural BPE-Free-to-BPE Drafter (50 MB)│ ├── stage13_2/ # Stage 13.2 Books Mastery 12-Slot Bridge + GSCA MoE (142 MB)│ ├── stage7_3_mujtahid_sovereign/ # Qwen3-1.7B Sovereign safetensors + tokenizer (3.3 GB, for CPU/GPU)│ └── stage12_bpe_free_foundation_best.pt # 12-Layer (101.55M) BPE-Free Foundation Model (388 MB)├── data/ # 329K Farāhīdī NMFE LUT, 2,469 Lexicon, Qur'an & Consensus Graph├── engine/ # Deterministic Khalīlian, Sībawayhian, Ṣarfī & Sabk engines├── scripts/ # Self-contained Stage 11–15.2 architecture & evaluation modules└── stage15_2/ # Stage 15.2 evaluation suites, training scripts & benchmark logs