--- 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](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) for local and edge deployment via [llama.cpp](https://github.com/ggerganov/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](https://huggingface.co/Qwen/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](https://huggingface.co/datasets/KonstantinDob/logic_inference_dataset) (~54,607 propositional logic pairs, LOGICINFERENCEe format). The model performs inference first, then concludes. Based on the [LogicInference paper](https://openreview.net/pdf?id=HAGeIS_Lcg9) by Santiago Ontañón (Google Research). | Attribute | Value | |---|---| | **Base model** | [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) | | **Teacher model** | [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/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](https://convergentintel.com): Research Division | ## Usage ### llama.cpp CLI ```bash ./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 ```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 ```bash 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](https://lmstudio.ai/). ## 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](https://huggingface.co/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](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled) | Stage 1 only | | [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) | Full precision source | | [Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF) | Instruct teacher + legal SFT GGUF | ## Citation ```bibtex @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](https://huggingface.co/reaperdoesntknow) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)* # ## 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](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) | 302 | HF | ### Top Models from Our Lab | Model | Downloads | |-------|-----------| | [Qwen3-1.7B-Thinking-Distil](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil) | 501 | | [LFM2.5-1.2B-Distilled-SFT](https://huggingface.co/reaperdoesntknow/LFM2.5-1.2B-Distilled-SFT) | 342 | | [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | 203 | | [Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF) | 175 | | [SMOLM2Prover-GGUF](https://huggingface.co/reaperdoesntknow/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](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c)** 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)](https://doi.org/10.57967/hf/8165) ### Related in this series - [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) (508 downloads) --- Part of the [reaperdoesntknow research portfolio](https://huggingface.co/reaperdoesntknow) — 49 models, 22,598 total downloads | Last refreshed: 2026-03-30 12:05 UTC