library_name, license, language, base_model, tags, pipeline_tag

library_name	license	language	base_model	tags	pipeline_tag
llama.cpp	apache-2.0	en	reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT	gguf quantized distillation sft reasoning mathematics physics legal stem chain-of-thought edge mobile convergentintel knowledge-distillation	text-generation

Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT — GGUF

GGUF quantizations of reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT for local, mobile, and edge deployment via llama.cpp and compatible runtimes.

A 30B Thinking teacher compressed 50x into a model that fits on a smartwatch.

Available Quantizations

File	Quant	Size	Use Case
qwen3-0.6b-distilled-30b-thinking-sft-f16.gguf	F16	~1.3 GB	Full precision reference
qwen3-0.6b-distilled-30b-thinking-sft-Q8_0.gguf	Q8_0	~700 MB	Near-lossless, desktop/laptop
qwen3-0.6b-distilled-30b-thinking-sft-Q5_K_M.gguf	Q5_K_M	~500 MB	Balanced, mobile
qwen3-0.6b-distilled-30b-thinking-sft-Q4_K_M.gguf	Q4_K_M	~400 MB	Smallest, IoT/edge/smartwatch

Recommended: Q5_K_M for mobile, Q4_K_M for maximum compression.

About the Model

Two-stage build:

Stage 1 — Thinking Teacher Distillation: Qwen3-0.6B distilled from Qwen3-30B-A3B-Thinking on 6,122 STEM chain-of-thought samples. The Thinking variant teacher produces extended reasoning traces with higher-entropy distributions, transferring richer deliberation structure into the student. Proof-weighted cross-entropy (2.5x → 1.5x on derivation tokens) + KL divergence at T=2.0.

Stage 2 — Legal SFT: Supervised fine-tuning on Alignment-Lab-AI/Lawyer-Instruct at conservative learning rate (5e-6) to layer legal reasoning on top of the STEM backbone without overwriting it.

Attribute	Value
Base model	Qwen/Qwen3-0.6B
Teacher model	Qwen/Qwen3-30B-A3B-Thinking-2507
Compression	50x parameters, ~75x with Q4_K_M
Developer	Reaperdoesntrun / Convergent Intelligence LLC: Research Division

Usage

llama.cpp CLI

./llama-cli -m qwen3-0.6b-distilled-30b-thinking-sft-Q4_K_M.gguf \
  -p "### Instruction:\nWhat is promissory estoppel?\n\n### Response:\n" \
  -n 512 --temp 0.0

llama.cpp Python

from llama_cpp import Llama

llm = Llama(model_path="qwen3-0.6b-distilled-30b-thinking-sft-Q4_K_M.gguf", n_ctx=1024)

output = llm(
    "### Instruction:\nProve that the square root of 2 is irrational.\n\n### Response:\n",
    max_tokens=512,
    temperature=0.0,
)
print(output["choices"][0]["text"])

Ollama

echo 'FROM ./qwen3-0.6b-distilled-30b-thinking-sft-Q4_K_M.gguf' > Modelfile
ollama create stem-legal-tiny -f Modelfile
ollama run stem-legal-tiny "Explain the difference between a felony and a misdemeanor."

LM Studio

Download any GGUF file from this repo and load directly in LM Studio.

Prompt Formats

STEM derivation (Stage 1):

Solve the following problem carefully and show a rigorous derivation.

Problem:
[Your problem]

Proof:

Instruction-following (Stage 2):

### Instruction:
[Your question]

### Response:

Limitations

0.6B is a hard capacity constraint. The model trades depth for deployability — it will make errors that larger models avoid. Multi-step proofs beyond ~8 steps degrade. Legal reasoning covers general concepts but lacks nuance. Always verify critical outputs. This is not a substitute for formal proof verification, licensed legal counsel, or professional analysis.

Source Model

Full training methodology, hyperparameters, and the two-stage pipeline are documented in:

reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT

Mathematical Foundations

This is a GGUF-quantized variant. The mathematical foundations (Discrepancy Calculus, Topological Knowledge Distillation) are documented in the source model's card. The discrepancy operator Df(x) and BV decomposition that inform the training pipeline are preserved through quantization — the structural boundaries detected by DISC during training are baked into the weights, not dependent on precision.

Related Models

Model	Description
Qwen3-0.6B-STEM-Proof-Distilled-Thinking	Stage 1 only — pure STEM backbone
Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT	Full precision source model
Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF	Larger 1.7B variant GGUF

Citation

@misc{colca2026thinking06bgguf,
  title={Qwen3-0.6B Distilled Thinking SFT: 50x Compression GGUF for Edge Deployment},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF},
  note={Convergent Intelligence LLC: Research Division}
}

---

Convergent Intelligence LLC: Research Division "Where classical analysis fails to see, we begin."

---

Convergent Intelligence Portfolio

Part of the Qwen3 0.6B Distillation Series by Convergent Intelligence LLC: Research Division

Mathematical Foundations

This is a GGUF-quantized variant. The mathematical foundations (Discrepancy Calculus, Topological Knowledge Distillation) are documented in the source model's card. The discrepancy operator Df(x) and BV decomposition that inform the training pipeline are preserved through quantization — the structural boundaries detected by DISC during training are baked into the weights, not dependent on precision.

Related Models

Model	Downloads	Format
Qwen3-0.6B-Distilled-30B-A3B	36	HF
Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT	33	HF

Top Models from Our Lab

Model	Downloads
Qwen3-1.7B-Thinking-Distil	501
LFM2.5-1.2B-Distilled-SFT	342
Qwen3-1.7B-Coder-Distilled-SFT	302
Qwen3-1.7B-Coder-Distilled-SFT-GGUF	194
Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF	175

Total Portfolio: 41 models | 2,781 total downloads

Last updated: 2026-03-28 12:49 UTC

DistilQwen Collection

This model is part of the DistilQwen proof-weighted distillation series. Collection: 9 models | 2,788 downloads

Teacher Variant Comparison

Teacher	Student Size	Strength	Models
Qwen3-30B-A3B (Instruct)	1.7B	Instruction following, structured output, legal reasoning	3 (833 DL)
Qwen3-30B-A3B (Thinking)	0.6B	Extended deliberation, higher-entropy distributions, proof derivation	3 (779 DL) ← this model
Qwen3-30B-A3B (Coder)	1.7B	Structured decomposition, STEM derivation, logical inference	2 (825 DL)

Methodology

The only BF16 collection in the portfolio. While the broader Convergent Intelligence catalog (43 models, 12,000+ downloads) was trained on CPU at FP32 for $24 total compute, the DistilQwen series was trained on H100 at BF16 with a 30B-parameter teacher. Same methodology, premium hardware. This is what happens when you give the pipeline real compute.

All models use proof-weighted knowledge distillation: 55% cross-entropy with decaying proof weights (2.5× → 1.5×), 45% KL divergence at T=2.0. The proof weight amplifies loss on reasoning-critical tokens, forcing the student to allocate capacity to structural understanding rather than surface-level pattern matching.

Full methodology: Structure Over Scale (DOI: 10.57967/hf/8165)

Related in this series

• Qwen3-0.6B-Distilled-30B-A3B (236 downloads)
• Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT (227 downloads)

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_{Part of the reaperdoesntknow research portfolio — 49 models, 22,598 total downloads | Last refreshed: 2026-03-30 12:05 UTC}