--- library_name: transformers pipeline_tag: text-generation license: apache-2.0 language: - en base_model: Qwen/Qwen3-0.6B datasets: - 0xZee/dataset-CoT-Advanced-Calculus-268 - 0xZee/dataset-CoT-Modern-Physics-177 - 0xZee/dataset-CoT-Theoretical-Mechanics-307 - 0xZee/dataset-CoT-Linear-Algebra-667 - 0xZee/dataset-CoT-Electromagnetism-580 - 0xZee/dataset-CoT-Molecular-Biology-71 - 0xZee/dataset-CoT-Physiology-114 - 0xZee/dataset-CoT-Classical-Mechanics-343 - 0xZee/dataset-CoT-Differential-Equations-636 - 0xZee/dataset-CoT-Physics-2254 - 0xZee/dataset-CoT-Engineering-574 - 0xZee/dataset-CoT-mathematics - Alignment-Lab-AI/Lawyer-Instruct tags: - causal-lm - text-generation - distillation - knowledge-distillation - sft - reasoning - chain-of-thought - mathematics - physics - engineering - legal - stem - convergentintel - edge --- # Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT A 0.6B parameter model built in two stages: knowledge distillation from a 30B Thinking teacher to establish a structured reasoning backbone, then supervised fine-tuning on legal instruction data. 50x compression. Under 500MB quantized. Runs on a phone. The training order is the thesis: teach the model *how to reason* first (distillation from Thinking teacher), then teach it *what to reason about* (legal SFT). The Thinking teacher's extended deliberation traces transfer deeper reasoning structure than an Instruct teacher — critical when the student has only 0.6B parameters to work with. > *"Structure beats scale, collaboration beats hierarchy, observation beats theory."* > — Convergent Intelligence LLC: Research Division ## Training Pipeline ### Stage 1: Knowledge Distillation (STEM Reasoning Backbone) Qwen3-0.6B distilled from [Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507) — a Mixture-of-Experts model with 30B total parameters, ~3B active per token, using the Thinking variant that generates extended internal reasoning traces. **Why the Thinking teacher matters at 0.6B:** The Thinking variant produces higher-entropy softmax distributions than the Instruct variant — it considers more reasoning paths before committing. At distillation temperature T=2.0, the 0.6B student sees a richer landscape of alternative derivation strategies. With only 0.6B parameters, every bit of transferred structure counts. The Thinking teacher gives more. **Data:** 6,122 STEM chain-of-thought samples across 12 domains: | Domain | Samples | |---|---| | Physics | 2,254 | | Linear Algebra | 667 | | Differential Equations | 636 | | Electromagnetism | 580 | | Mathematics | 576 | | Engineering | 574 | | Classical Mechanics | 343 | | Theoretical Mechanics | 307 | | Advanced Calculus | 268 | | Modern Physics | 177 | | Physiology | 114 | | Molecular Biology | 71 | All from [0xZee](https://huggingface.co/0xZee). Shuffled seed 42, split 95/5 train/eval. **Loss function:** 1. **Proof-Weighted Cross-Entropy (55%)** — 2.5x weight on derivation tokens, decaying to 1.5x. Forces the student to allocate its limited capacity to reasoning steps, not answer formatting. 2. **Knowledge Distillation KL Divergence (45%)** — T=2.0, scaled by T². Transfers the Thinking teacher's full deliberation landscape. **Training format:** ``` Solve the following problem carefully and show a rigorous derivation. Problem: {question} Proof: {CoT} Final Answer: {response} ``` **Stage 1 hyperparameters:** | Parameter | Value | |---|---| | Epochs | 1 | | Training samples | 5,815 | | Effective batch size | 8 | | Learning rate | 1.5e-5 → 1e-6 (cosine) | | Temperature | 2.0 | | Proof weight | 2.5 → 1.5 | | Precision | bf16 | --- ### Stage 2: Supervised Fine-Tuning (Legal Domain) The distilled model was fine-tuned on [Alignment-Lab-AI/Lawyer-Instruct](https://huggingface.co/datasets/Alignment-Lab-AI/Lawyer-Instruct) using TRL's SFTTrainer. **Why legal on top of STEM:** Legal reasoning is structurally isomorphic to mathematical reasoning — premise identification, logical chaining, exception handling, structured argumentation toward a conclusion. A model that learned rigorous derivation transfers that structure to legal analysis rather than learning legal templates from scratch. **Training format:** ``` ### Instruction: {instruction} ### Response: {output} ``` **Stage 2 hyperparameters:** | Parameter | Value | |---|---| | Epochs | 1 | | Effective batch size | 8 | | Learning rate | 5e-6 (lower than Stage 1 to preserve backbone) | | Gradient checkpointing | Enabled | | Precision | bf16 | ## Model Details | Attribute | Value | |---|---| | **Architecture** | Qwen3 (causal LM, RoPE, GQA) | | **Parameters** | 0.6B | | **Base model** | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) | | **Teacher model** | [Qwen/Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507) | | **Compression ratio** | 50x | | **Stage 1 data** | 6,122 STEM CoT samples (12 datasets) | | **Stage 2 data** | [Alignment-Lab-AI/Lawyer-Instruct](https://huggingface.co/datasets/Alignment-Lab-AI/Lawyer-Instruct) | | **Context length** | 1024 tokens (training) | | **License** | Apache 2.0 | | **Developer** | Reaperdoesntrun / [Convergent Intelligence LLC](https://convergentintel.com): Research Division | ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32, device_map="auto", ) # Legal instruction-following prompt = """### Instruction: What is the difference between a felony and a misdemeanor? ### Response: """ # STEM derivation (Stage 1 format still works) prompt_stem = """Solve the following problem carefully and show a rigorous derivation. Problem: Compute the determinant of the matrix [[1, 2], [3, 4]]. Proof: """ inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ### GGUF Quantized versions at [reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF). ## Prompt Formats **STEM derivation (Stage 1):** ``` Solve the following problem carefully and show a rigorous derivation. Problem: [Your problem] Proof: ``` **Instruction-following (Stage 2):** ``` ### Instruction: [Your question] ### Response: ``` ## Intended Uses **Good for:** Ultra-lightweight reasoning on mobile/edge/IoT, legal and STEM instruction-following, educational tutoring, embedded inference, component in multi-model pipelines, anywhere you need reasoning in under 500MB. **Not for:** Formal proof verification, actual legal counsel, safety-critical analysis, complex multi-step proofs (>8 steps), or long-context tasks beyond 1024 tokens. ## Limitations 0.6B is a hard capacity constraint. The model trades depth for deployability. It will make reasoning errors that a larger model would not. Multi-step derivations beyond ~8 steps degrade. Legal reasoning covers general concepts but lacks the nuance of larger models. Performance is weakest on underrepresented domains (molecular biology, physiology). Always verify outputs. ## Mathematical Foundations: Discrepancy Calculus (DISC) This model is part of a distillation chain built on Discrepancy Calculus — a measure-theoretic framework where the teacher's output distribution is decomposed via the Mesh Fundamental Identity into smooth (AC), jump, and Cantor components. The discrepancy operator $Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varepsilon} \frac{|f(t) - f(x)|}{|t - x|} dt$ quantifies local structural mismatch that standard KL divergence averages away. Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division). Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165). ## Related Models | Model | Description | |---|---| | [Qwen3-0.6B-STEM-Proof-Distilled-Thinking](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-STEM-Proof-Distilled-Thinking) | Stage 1 only — pure STEM backbone | | [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | This model quantized for edge deployment | | [Qwen3-1.7B-STEM-Proof-Distilled](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-STEM-Proof-Distilled) | Larger 1.7B variant (Instruct teacher) | | [Qwen3-1.7B-Distilled-30B-A3B-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT) | Larger 1.7B variant + legal SFT | ## Citation ```bibtex @misc{colca2026thinking06bsft, title={Two-Stage Reasoning Transfer at 0.6B: Thinking Teacher Distillation + Legal SFT}, year={2026}, publisher={HuggingFace}, url={https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT}, 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 0.6B Distillation Series](https://huggingface.co/reaperdoesntknow) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)* # ## Mathematical Foundations: Discrepancy Calculus (DISC) This model is part of a distillation chain built on Discrepancy Calculus — a measure-theoretic framework where the teacher's output distribution is decomposed via the Mesh Fundamental Identity into smooth (AC), jump, and Cantor components. The discrepancy operator $Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varepsilon} \frac{|f(t) - f(x)|}{|t - x|} dt$ quantifies local structural mismatch that standard KL divergence averages away. Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division). Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165). ## Related Models | Model | Downloads | Format | |-------|-----------|--------| | [Qwen3-0.6B-Distilled-30B-A3B](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B) | 36 | HF | | [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | 203 | GGUF | ### 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 | | [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) | 302 | | [Qwen3-1.7B-Coder-Distilled-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT-GGUF) | 194 | | [Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF) | 175 | **Total Portfolio: 41 models | 2,781 total downloads** *Last updated: 2026-03-28 12:56 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) **← this model** | | 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 - [Qwen3-0.6B-Distilled-30B-A3B](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B) (236 downloads) - [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) (316 downloads)