Qwen3-1.7B

Model · Text generation

TopologicalQwen

Convergent Intelligence

Topology-Aware Knowledge Distillation from Qwen3-30B-A3B → 1.7B TopologicalQwen is a 1.7B parameter model distilled from Qwen3-30B-A3B using Topological Knowledge Distillation (TKD) — a methodology that treats the teacher's output distribution over a concatenated token stream as a bounded variation (BV) function and decomposes knowledge transfer into three channels via the Mesh Fundamental Identity: 1. Smooth distillation (AC component) — Standard KL divergence over regions where the teacher's distribution varies continuously. This is what every other KD method does and stops at. 2. Jump corrections (D^j f) — Explicit correction terms at conceptual boundaries where the teacher's…

Model · Text generation

Qwen3-1.7B-Thinking-Distil

Convergent Intelligence

Extended Reasoning Distillation from Qwen3-30B-A3B-Thinking → 1.7B The most downloaded model in the Convergent Intelligence portfolio. Qwen3-1.7B-Thinking-Distil captures extended deliberation patterns from the Qwen3-30B-A3B Thinking teacher — the variant that generates long-form reasoning chains before committing to an answer — and compresses them into a 1.7B student via supervised fine-tuning on the longwriter-6k dataset. The Thinking teacher produces the richest signal of the three teacher variants in the DistilQwen family (Instruct, Thinking, Coder). Where Instruct distillation captures clean instruction-following and Coder captures hierarchical decomposition, Thinking distillation…

Model · Text generation

Qwen3-1.7B-Distilled-30B-A3B

Convergent Intelligence

A 1.7B-parameter causal language model distilled from Qwen3-30B-A3B on 6,122 STEM chain-of-thought samples using discrepancy-informed knowledge distillation. The training objective emphasizes proof structure, detects reasoning pivot tokens through token-level divergence dynamics, smooths high-entropy student singularities before distillation, and monitors structural drift through discrepancy energy. Standard knowledge distillation treats all tokens uniformly. Even proof-weighted approaches typically apply a static multiplier over the entire derivation span. That helps, but it still misses the internal structure of reasoning: some regions are smooth procedural continuation, while others are…

Model · Text generation

Qwen3-1.7B-Coder-Distilled-SFT

Convergent Intelligence

A 1.7B model built in two stages: knowledge distillation from a 30B Coder teacher to establish a structured reasoning backbone, then supervised fine-tuning on ~54,600 logical inference problems. The Coder teacher's decomposition patterns meet formal propositional logic. The hypothesis: a model that learned STEM derivation from a Coder teacher (Stage 1) already has latent structure for sequential logic, state tracking, and compositional reasoning. Logical inference SFT (Stage 2) activates that structure explicitly — the model doesn't learn logic from scratch, it surfaces what the Coder teacher already gave it. Qwen3-1.7B distilled from Qwen3-Coder-30B-A3B-Instruct — the coding-specialized…

Model · Text generation

DistilQwen3-1.7B-uncensored

Convergent Intelligence

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). Part of the DistilQwen3 Series by Convergent Intelligence LLC: Research Division 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…

Model · Text generation

Dualmind-Qwen-1.7B-Thinking

Convergent Intelligence

Claude Opus 4.6 Reasoning Traces → 1.7B via DualMind SFT A 1.7B model trained on 2.5M+ tokens of Claude Opus 4.6 reasoning traces using the DualMind SFT methodology. The training data comes from Opus-4.6-Reasoning-3000x-filtered — a curated dataset of extended reasoning chains from Anthropic's most capable model, with refusals removed. This is the Opus variant of the DualMind family. Where the base DualMind model was trained on LogicInference data, this model absorbs the reasoning patterns of Claude Opus 4.6 — longer chains, more nuanced self-correction, and richer deliberative structure. The Opus teacher produces qualitatively different reasoning than synthetic logic datasets: it…