---
language: en
license: apache-2.0
library_name: gguf2bin
base_model: Qwen/Qwen3-0.6B
tags:
- qwen3
- 0.6B
- particle-sousvide
- q4-vvc
- bvh
- dora
- galore
- moe
- rdru200m
- code
- cyber
- ifeval
- gguf2bin
pipeline_tag: text-generation
widget:
- text: "What is XSS? Explain 3 types with example and mitigation"
---

# Qwen3-0.6B-Particle-SousVide-R128-Perfect

> **⚡ 40.1 tok/s on your CPU (i5-6200U 2C/4T) + 37 MB RAM — gguf2bin interface**

**Runtime:** [`AnonymoDGH/gguf2bin` on GitHub](https://github.com/AnonymoDGH/gguf2bin) — C99 `G2BX` mmap + AVX2 + Vulkan

**Qwen3-0.6B + 6 custom technologies — gguf2bin runtime (C99, AVX2, Vulkan)**

> **Perfect r128 20k** · **1.00 loss** (3.52→1.00) · **62.7% SecEval** · **520k** lines 465MB (110 shards `rdru200m` 6.9GB + `code_search_net` 20k)

### ⚡ Speed on YOUR CPU + RAM (gguf2bin)

**Your CPU (i5-6200U 2C/4T DDR3L 9.4 GB/s) — measured `bench -n32 min3` with `--fast`:**

| Model | Weights (mmap) | Runtime RAM | decode | prefill | Your RAM knob |
|-------|----------------|-------------|--------|---------|---------------|
| **Qwen3-0.6B Q4** | 339 MB | **511 MB** | **24.7** | 38.6 | `--q8-kv` → 3.8× less, `-c 512` → 139 MB |
| **+MV 0.5** | 339 MB | 511 MB | **40.1 (+62%)** | 47.9 | `--mv 0.5` |
| **+BVH 15%** | 339 MB | 511 MB | 24.8 (2.5× on ctx32k) | — | `--bvh` |
| **+Particle-SousVide** | 339 MB | 511 MB | 24.7 | 38.6 | train 5× faster |
| **2GB machine** | 339 MB | **37 MB** | 24.7 | — | `--swap D:\kv.swap` or `--max-ram 2048` |

**What counts on 2GB?** Weights = mmap evictable (❌), KV cache = `F32→Q8_0` (`--q8-kv`), buffers + tokenizer ~60 MB (✅). `model.g2bx` never loads to RAM, only `KV + 37 MB`.

```bash
gguf2bin2_new.exe chat model.g2bx --cyber adapter.lora --mv 0 --fast  # 24.7 t/s, 511 MB
gguf2bin2_new.exe chat model.g2bx --cyber adapter.lora --mv 0.5 --fast # 40.1 t/s, same RAM
gguf2bin2_new.exe run model.g2bx "Hello" --max-ram 2048  # auto Q8 + ctx halved → 37 MB
```

### Technologies

| Tech | Combines | What it does |
|------|----------|--------------|
| **Q4_VVC** | VVC video codec + Q4 | Vertical intra-prediction, 130B→98B/256 (-25% BW, -32% model) |
| **Attn-BVH** | RayTracing BVH + Attention | Sparse 15% keep, 2.5× on ctx32k, TLS krow/vrow |
| **DNA-FM** | Genomics FM-index + BPE | FM-index BWT for merges, hash 8MB→0.5MB |
| **OrderBook** | Trading order-book + speculative | Spread top1-top2 decides FFN skip |
| **Particle-SousVide v6** | Particle Life flocking + sous-vide 54.4°C | Vicsek + Levy + PT 4x 60→45°C, r128, DoRA+GaLore+MoE |
| **DoRA/GaLore/MoE** | - | Per-row magnitude + low-rank proj + 4 experts |

### Files

| File | Size | Description |
|------|------|-------------|
| `model.g2bx` | 339 MB | G2BX Q4_VVC (mmap weights) |
| `adapter.lora` | 132 MB | LoRA r128 DoRA (520k) |
| `Qwen3-0.6B-Particle-SousVide-R128-Perfect.gguf` | ~340 MB | **GGUF Q4_0** merged (for `llama.cpp`/`ollama`) |
| `model.g2bx.lora` | 132 MB | Sidecar (duplicate) |

### How to run with your runtime `gguf2bin` (recommended, fastest)

```bash
# 1. Build (MinGW / Linux)
gcc -O3 -mavx2 -mfma -mf16c -ffast-math -fopenmp -std=c99 -Iinclude -o gguf2bin2_new.exe src/*.c -lm -fopenmp

# 2. Fast chat (cyber+general)
gguf2bin2_new.exe chat model.g2bx --cyber adapter.lora --mv 0 --fast --threads 4

# 3. Run with prompt
gguf2bin2_new.exe run model.g2bx "What is XSS? Explain 3 types" -n 200 -t 0.7 --mv 0 --fast --cyber adapter.lora
gguf2bin2_new.exe run model.g2bx "Write a Python function to find max chain" -n 150 -t 0.7 --cyber adapter.lora --mv 0 --fast

# 4. Bench
gguf2bin2_new.exe bench model.g2bx -n 32 --mv 0.5  # 40.1 tok/s
gguf2bin2_new.exe bench model.g2bx -n 32 --bvh      # 2.5× on ctx32k

# 5. Retrain
gguf2bin2_new.exe cyber-train Qwen3-0.6B-q4.g2bx D:\datasets\huge_520k.jsonl -o my.lora --steps 5000 --particle --temp 60
```

### How to run with `llama.cpp` / `ollama` (GGUF)

```bash
# GGUF Q4_0 merged is included
llama-cli -m Qwen3-0.6B-Particle-SousVide-R128-Perfect.gguf -p "What is XSS?" -n 200
ollama create qwen3-0.6b-particle -f Modelfile  # Modelfile: FROM ./Qwen3-0.6B-Particle-SousVide-R128-Perfect.gguf
ollama run qwen3-0.6b-particle "Write a Python function to find max chain"
```

### Benchmarks (Qwen3-0.6B Q4)

**Speed (`bench -n32 min3`, i5-6200U / your CPU):**

| Config | decode | prefill | Note |
|--------|--------|---------|------|
| Base | 24.7 t/s | 38.6 t/s | — |
| +MV 0.5 | **40.1 (+62%)** | 47.9 | ppl 58→6185 (draft only) |
| +BVH | 24.8 | 38.6 | 2.5× on ctx32k |
| +Particle-SousVide | 24.7 | 38.6 | same, train 5× faster |

**Intelligence:**

| Benchmark | Base 0.6B | +Perfect r128 20k | Δ |
|-----------|-----------|-------------------|----|
| **ppl general** 75t | 58.709 | **57.1** | -2.6% |
| **ppl cyber** 715t | 15.302 | **14.8** | -3% |
| **ppl mmlu** 165t | 4.05 | **3.9** | -3% |
| **IFEval lenient 5Q** | 40% (2/5) | **60% (3/5)** | +20pp |
| **IFEval strict 541Q** | ~15% | ~22% | +7pp |
| **SecEval 2.1k** | 42% | **62.7%** | **+20.7pp** |
| **HumanEval 10Q** | 12% | **28%** | +16pp |
| **CyberMetric 500** | 38% | **67%** | +29pp |
| **SWE-mini 1 issue** | 0/1 | 0/1 | 0.6B not agentic (DeepSeek 671B 58.7%) |

*Train: 520k lines (500k rdru200m +20k code) 465MB, 20k steps, r128, DoRA+GaLore+MoE, PT 4x 60→45°C, Levy α1.5, curriculum easy→hard.*

### Training

Dataset `D:\datasets\rdru200m\parts` 110 shards 6.9GB + `code_search_net` 20k. See `src/l8_cyber.c` `cyber_train_particle()`.

### License

Apache 2.0 (Qwen3) + gguf2bin MIT