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Model: reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF
Source: Original Platform

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README.md

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+---
+library_name: llama.cpp
+license: apache-2.0
+language:
+  - en
+base_model: reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT
+tags:
+  - gguf
+  - quantized
+  - distillation
+  - sft
+  - reasoning
+  - mathematics
+  - physics
+  - legal
+  - stem
+  - chain-of-thought
+  - convergentintel
+  - edge
+  - knowledge-distillation
+pipeline_tag: text-generation
+---
+
+# Qwen3-1.7B-Distilled-30B-A3B-SFT — GGUF
+
+GGUF quantizations of [reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT) for local and edge deployment via [llama.cpp](https://github.com/ggerganov/llama.cpp) and compatible runtimes.
+
+## Available Quantizations
+
+| File | Quant | Size | Description |
+|---|---|---|---|
+| `qwen3-1.7b-stem-proof-f16.gguf` | F16 | ~3.8 GB | Full precision reference |
+| `qwen3-1.7b-distilled-30b-sft-Q8_0.gguf` | Q8_0 | ~2.1 GB | Near-lossless, desktop |
+| `qwen3-1.7b-distilled-30b-sft-Q5_K_M.gguf` | Q5_K_M | ~1.4 GB | Balanced quality and size |
+| `qwen3-1.7b-distilled-30b-sft-Q4_K_M.gguf` | Q4_K_M | ~1.2 GB | Mobile, edge, fastest inference |
+
+**Recommended:** Q5_K_M for desktop use, Q4_K_M for mobile/edge.
+
+## About the Model
+
+This is a two-stage model:
+
+**Stage 1 — DISC-Informed Knowledge Distillation:** Qwen3-1.7B distilled from Qwen3-30B-A3B-Instruct on 6,122 STEM chain-of-thought samples using proof-weighted cross-entropy loss (2.5x → 1.5x decay on derivation tokens) and KL divergence at T=2.0. The distillation emphasized multi-step reasoning over final-answer pattern matching.
+
+**Stage 2 — Legal SFT:** Follow-up supervised fine-tuning on [Alignment-Lab-AI/Lawyer-Instruct](https://huggingface.co/datasets/Alignment-Lab-AI/Lawyer-Instruct) to add instruction-following capability and legal domain knowledge on top of the STEM reasoning backbone.
+
+The result is a 1.7B model that fits on a phone and can do structured derivations, legal reasoning, and instruction-following.
+
+| Attribute | Value |
+|---|---|
+| **Base model** | [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) |
+| **Teacher model** | [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) |
+| **Distillation data** | 6,122 STEM CoT samples (12 datasets from [0xZee](https://huggingface.co/0xZee)) |
+| **SFT data** | [Alignment-Lab-AI/Lawyer-Instruct](https://huggingface.co/datasets/Alignment-Lab-AI/Lawyer-Instruct) |
+| **Developer** | Reaperdoesntrun / [Convergent Intelligence LLC](https://convergentintel.com): Research Division |
+
+## Usage
+
+### llama.cpp CLI
+
+```bash
+./llama-cli -m qwen3-1.7b-distilled-30b-sft-Q4_K_M.gguf \
+  -p "### Instruction:\nExplain the doctrine of promissory estoppel and provide a worked example.\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-distilled-30b-sft-Q4_K_M.gguf", n_ctx=1024)
+
+output = llm(
+    "### Instruction:\nProve that the sum of two even numbers is even.\n\n### Response:\n",
+    max_tokens=512,
+    temperature=0.0,
+)
+print(output["choices"][0]["text"])
+```
+
+### Ollama
+
+```bash
+# Create a Modelfile
+echo 'FROM ./qwen3-1.7b-distilled-30b-sft-Q4_K_M.gguf' > Modelfile
+ollama create stem-legal -f Modelfile
+ollama run stem-legal "What is res judicata?"
+```
+
+### LM Studio
+
+Download any GGUF file from this repo and load it directly in [LM Studio](https://lmstudio.ai/).
+
+## Prompt Formats
+
+This model responds to two prompt formats from its two training stages:
+
+**STEM derivation (from distillation):**
+
+```
+Solve the following problem carefully and show a rigorous derivation.
+
+Problem:
+[Your math/physics/engineering problem]
+
+Proof:
+```
+
+**Instruction-following (from SFT):**
+
+```
+### Instruction:
+[Your question or task]
+
+### Response:
+```
+
+## Limitations
+
+This is a 1.7B model — it punches above its weight on structured reasoning but has hard limits. It can produce fluent but incorrect derivations. It is not a substitute for formal proof verification, legal counsel, or professional engineering analysis. Verify all outputs independently. Performance is strongest on physics, differential equations, and legal instruction-following. Weaker on underrepresented domains (molecular biology, physiology).
+
+## Source Model
+
+Full training details, methodology, hyperparameters, and the DISC-informed distillation approach are documented in the source model card:
+
+**[reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT)**
+
+## Citation
+
+```bibtex
+@misc{colca2026distilledsft,
+  title={Qwen3-1.7B Distilled 30B-A3B SFT: STEM Reasoning + Legal Instruction Following},
+  year={2026},
+  publisher={HuggingFace},
+  url={https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-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 1.7B Distillation 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-Distilled-30B-A3B](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B) | 96 | HF |
+| [Qwen3-1.7B-Distilled-30B-A3B-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT) | 65 | HF |
+| [Qwen3-1.7B-Thinking-Distil](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil) | 501 | HF |
+
+### Top Models from Our Lab
+
+| Model | Downloads |
+|-------|-----------|
+| [LFM2.5-1.2B-Distilled-SFT](https://huggingface.co/reaperdoesntknow/LFM2.5-1.2B-Distilled-SFT) | 342 |
+| [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) | 302 |
+| [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-Coder-Distilled-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT-GGUF) | 194 |
+| [SMOLM2Prover-GGUF](https://huggingface.co/reaperdoesntknow/SMOLM2Prover-GGUF) | 150 |
+
+**Total Portfolio: 41 models | 2,781 total downloads**
+
+
+*Last updated: 2026-03-28 12:55 UTC*
+
+<!-- DISTILQWEN-SPOTLIGHT-START -->
+
+## 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) **← this model** |
+| 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) |
+
+### 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-Distilled-30B-A3B](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B) (292 downloads)
+- [Qwen3-1.7B-Distilled-30B-A3B-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT) (252 downloads)
+
+<!-- DISTILQWEN-SPOTLIGHT-END -->
+<!-- cix-keeper-ts:2026-04-11T16:09:25Z -->
+<!-- card-refresh: 2026-03-30 -->

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