--- 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 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)