Qwen3Loop-0.6B-SFT-Deep-Supervision (v1.0 Production: Eurus-2 + Open-R1 CoT & Adaptive Halting)
Qwen3Loop-0.6B is a state-of-the-art recursive reasoning language model utilizing recurrent looped layers to achieve the reasoning density and depth of a ~2B parameter model within a 0.6B physical parameter footprint.
🌟 Core Architecture & Innovation
- Physical Parameters: 28 transformer blocks (
hidden_size: 1024,intermediate_size: 3072,heads: 16). - LoopSplit Architecture:
- Prefix: Layers 0..6 (computed once).
- Middle Stack (Miolo): Layers 7..20 (computed 3 times recurrently = 42 layer passes).
- Suffix: Layers 21..27 (computed once).
- Total Logical Depth: 56 layers ($7 + 14 \times 3 + 7 = 56$).
- Curated Multi-Phase Dataset: Fine-tuned on 6,324 strictly filtered and curated samples (25.3 MB) from
PRIME-RL/Eurus-2-SFT-Data(agentic multi-phase) andopen-r1/Mixture-of-Thoughts(coding CoT), annotated across 9 meta-supervision dimensions withconvaiinnovations/laya. - Adaptive Halting Probe: Includes
latent_halting_probe.pt, an ultralight MLP head trained directly on intermediate prompt prefill states to decide optimal exit depth with 91.5% classification accuracy.
📊 Training & Validation Metrics
| Metric | Result | Context / Meaning |
|---|---|---|
| Validation Loss | 0.4012 | Down from 0.6705 (smooth, stable convergence) |
| Perplexity (PPL) | 1.49 | Near-optimal linguistic and logical fluency |
| Hardware Stability | 5.58 GB VRAM | Flat VRAM curve throughout 375 steps on RTX 3060 12GB |
| Halting Probe Routing Acc | 91.5% | Calibrated on prompt prefill representations (MSE: 0.0015) |
🥊 Direct Confrontation: Base 28-Layer vs Qwen3Loop 56-Layer Unrolled
Evaluated under identical sampling parameters (temperature=0.1, Q8_0 quantization on RTX 3060):
| Challenge Task | Base Unsloth (Qwen3-0.6B-Q8_0) [28L] | Qwen3Loop SFT (unrolled_q8_0) [56L] | Outcome |
|---|---|---|---|
| Modular Arithmetic($2^{100} \pmod 7$) | Incomplete / Verbose divergence(Overflowed token budget without answer) | Exact: $\boxed{2}$ in 5 logical steps(Detected optimal order $2^3 \equiv 1 \pmod 7$) | 🟢 Qwen3Loop |
| Deductive Logic(Sally Siblings Riddle) | Trapped in self-doubt loop | Deduced correct sister relationship | 🟢 Qwen3Loop |
| Linguistic Trick(17 sheep, all but 9 die) | Arithmetic subtraction error ($17-9=8$) | Arithmetic subtraction error ($17-9=8$) | ⚪ Tie |
| Power Cycles(Units digit of $3^{2025}$) | Stopped at cycle definition | Full sequence & pattern $(3,9,7,1)$ | 🟢 Qwen3Loop |
| Algorithm Formulation(Balanced Parentheses) | Drafted stack concept, cut off | Counter-based $O(n)$ time / $O(1)$ space logic | 🟢 Qwen3Loop |
| Generation Speed | 255.6 tokens/s (28 layers) | 156.1 tokens/s (56 layers real pass) | Hardware verified |
📦 Model Files & Download Options
1. Universal Unrolled Models (Compatible with stock LM Studio, Ollama, llama.cpp)
Run natively out of the box without any custom forks or patches (architecture mapped to qwen3 with 56 logical layers):
unrolled_modelo_qwen3loop_sft_q8_0.gguf(1.11 GB): High-precision Q8_0 quantization. Recommended for all local chat engines.unrolled_modelo_qwen3loop_sft_f16.gguf(2.08 GB): Full-precision FP16 unrolled graph.
2. Compact Native Looped Models (For custom engines supporting cyclic execution)
modelo_qwen3loop_sft_q8_0.gguf(0.64 GB): Compact 28 physical blocks in Q8_0.modelo_qwen3loop_sft_f16.gguf(1.20 GB): Compact 28 physical blocks in FP16.
3. Standalone PyTorch Weights & Halting Probe
model.safetensors(1.14 GB): Production PyTorch weights incorporating the full SFT training.latent_halting_probe.pt(0.5 MB): Trained lightweight MLP probe for prefill-based dynamic depth routing.config.json,tokenizer.json,chat_template.jinja: Full tokenizer and architectural configuration.engine/: Python implementation and PyTorch forward hooks.
🎯 Recommended Sampling Parameters
{
"temperature": 0.6,
"top_p": 0.95,
"top_k": 40,
"repeat_penalty": 1.08,
"context_length": 32768
}
(For deterministic math and coding tasks, set temperature: 0.0 or 0.1).