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{cix2026codersftgguf,
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