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 — 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.

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)

# 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)

# 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