---

library_name: llama.cpp

license: apache-2.0

language:

- en

base_model: reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT

tags:

- gguf

- quantized

- distillation

- sft

- reasoning

- mathematics

- physics

- logic

- logical-inference

- stem

- code

- chain-of-thought

- convergentintel

- edge

- knowledge-distillation

pipeline_tag: 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

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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)

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

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