Qwen3-1.7B-Coder-Distilled-SFT-GGUF/README.md

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/Qwen3-1.7B-Coder-Distilled-SFT	gguf quantized distillation sft reasoning mathematics physics logic logical-inference stem code chain-of-thought convergentintel edge knowledge-distillation	text-generation

Qwen3-1.7B-Coder-Distilled-SFT — GGUF

GGUF quantizations of reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT for local and edge deployment via llama.cpp and compatible runtimes.

Coder teacher → STEM distillation → logical inference SFT → quantized. Structured reasoning in ~1.2GB.

Available Quantizations

File	Quant	Size	Use Case
qwen3-1.7b-coder-distilled-sft-f16.gguf	F16	~3.8 GB	Full precision reference
qwen3-1.7b-coder-distilled-sft-Q8_0.gguf	Q8_0	~2.1 GB	Near-lossless, desktop
qwen3-1.7b-coder-distilled-sft-Q5_K_M.gguf	Q5_K_M	~1.4 GB	Balanced quality and size
qwen3-1.7b-coder-distilled-sft-Q4_K_M.gguf	Q4_K_M	~1.2 GB	Mobile, edge, fastest inference

Recommended: Q5_K_M for desktop, Q4_K_M for mobile/edge.

About the Model

Two-stage build:

Stage 1 — Coder Teacher Distillation: Qwen3-1.7B distilled from Qwen3-Coder-30B-A3B-Instruct on 6,122 STEM CoT samples. Proof-weighted cross-entropy (2.5x → 1.5x on derivation tokens) + KL divergence at T=2.0. The Coder teacher transfers structured decomposition patterns — sequential logic, state tracking, compositional reasoning — through the softmax landscape.

Stage 2 — Logical Inference SFT: Fine-tuned on KonstantinDob/logic_inference_dataset (~54,607 propositional logic pairs, LOGICINFERENCEe format). The model performs inference first, then concludes. Based on the LogicInference paper by Santiago Ontañón (Google Research).

Attribute	Value
Base model	Qwen/Qwen3-1.7B
Teacher model	Qwen/Qwen3-Coder-30B-A3B-Instruct
Stage 1 data	6,122 STEM CoT samples
Stage 2 data	~54,607 logical inference pairs
Developer	Reaperdoesntrun / Convergent Intelligence LLC: Research Division

Usage

llama.cpp CLI

./llama-cli -m qwen3-1.7b-coder-distilled-sft-Q4_K_M.gguf \
  -p "### Instruction:\nConsider the premises: If it rains, the ground is wet. It is raining. What can we conclude?\n\n### Response:\n" \
  -n 512 --temp 0.0

llama.cpp Python

from llama_cpp import Llama

llm = Llama(model_path="qwen3-1.7b-coder-distilled-sft-Q4_K_M.gguf", n_ctx=1024)

output = llm(
    "### Instruction:\nIs the following argument valid? All dogs are animals. Some animals are pets. Therefore, all dogs are pets.\n\n### Response:\n",
    max_tokens=512,
    temperature=0.0,
)
print(output["choices"][0]["text"])

Ollama

echo 'FROM ./qwen3-1.7b-coder-distilled-sft-Q4_K_M.gguf' > Modelfile
ollama create logic-reasoner -f Modelfile
ollama run logic-reasoner "If all humans are mortal and Socrates is human, what follows?"

LM Studio

Download any GGUF file 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:

Logical inference / instruction-following (Stage 2):

### Instruction:
[Your question or logical inference problem]

### Response:

Limitations

1.7B model. Structured reasoning with hard capacity limits. Not a code generator despite the Coder teacher. Not a formal proof verifier. Complex multi-step inferences with many quantifiers may exceed capacity. Always verify critical outputs.

Source Model

Full training methodology at: reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-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
Qwen3-1.7B-Coder-Distilled	Stage 1 only
Qwen3-1.7B-Coder-Distilled-SFT	Full precision source
Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF	Instruct teacher + legal SFT GGUF

Citation

@misc{colca2026codersftgguf,
  title={Coder-Distilled Logical Inference GGUF: Structured Reasoning for Edge Deployment},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-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 Coder 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
Qwen3-1.7B-Coder-Distilled-SFT	302	HF

Top Models from Our Lab

Model	Downloads
Qwen3-1.7B-Thinking-Distil	501
LFM2.5-1.2B-Distilled-SFT	342
Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF	203
Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF	175
SMOLM2Prover-GGUF	150

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)
Qwen3-30B-A3B (Coder)	1.7B	Structured decomposition, STEM derivation, logical inference	2 (825 DL) ← this model

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

• Qwen3-1.7B-Coder-Distilled-SFT (508 downloads)

---

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