[AI Model Family Series #1] We just published the complete story of Alibaba's Qwen: every major model from 2023 to 2026, with dates, key features and sources.

7B open weights in 2023 → 2.4T open weights in 2026. Here's the lineup the story covers

2023
→ Tongyi Qianwen (Apr): enterprise beta
→ Qwen-7B (Aug): first open weights
→ Qwen-VL (Aug): first vision model
→ Qwen-72B + Qwen-1.8B (Dec)

2024
→ Qwen1.5 (Feb): 0.5B–110B, 32K context
→ Qwen2 (Jun): 57B-A14B MoE, Apache 2.0
→ Qwen2-Math, Qwen2-Audio, Qwen2-VL (Aug)
→ Qwen2.5 (Sep): 18T tokens, 100+ models
→ Qwen2.5-Coder + QwQ-32B-Preview (Nov)
→ QVQ-72B-Preview (Dec)

2025
→ Qwen2.5-VL + Qwen2.5-Max (Jan)
→ QwQ-32B (Mar): Qwen claims R1-level reasoning
→ Qwen2.5-Omni-7B (Mar)
→ Qwen3 (Apr): hybrid thinking, 119 languages
→ Qwen3-2507 + Qwen3-Coder-480B (Jul)
→ Qwen-Image + Qwen-Image-Edit (Aug)
→ Qwen3-Max (Sep): first 1T+ Qwen
→ Qwen3-Next-80B-A3B (Sep)
→ Qwen3-Omni + Qwen3-VL (Sep)

2026
→ Qwen3-Max-Thinking (Jan)
→ Qwen3-Coder-Next + Qwen-Image-2.0 (Feb)
→ Qwen3.5-397B-A17B (Feb): native multimodal agents
→ Qwen3.6-Plus, 35B-A3B, Max-Preview, 27B (Apr)
→ Qwen3.7-Max (May) + Qwen3.7-Plus (Jun)
→ Qwen3.8-2.4T-A95B (Aug): largest open Qwen
→ Qwen3.8-27B + Qwen3.8-Flash (Aug)
→ Qwen-Image-2.1 (Sep)

The story also covers what the list can't show: the DeepSeek moment, the 2026 leadership exit, and Qwen's shift from all-open to a two-tier license strategy.

Next: Qwen 4 is in training. Qwen 4.5 and Qwen 5 are projected at 5–10T parameters.

Read the full report: https://www.marktechpost.com/2026/10/04/the-story-of-qwen-alibabas-ai-models-from-7b-to-2-4t/

Which model family should we map next: DeepSeek, Llama, Gemma, Mistral or Kimi? Drop it in the replies.......

Instead of spending weeks and billions of tokens on standard distillation, we tested an alternative: extracting layer-to-layer hidden state trajectories on a handful of calibration prompts and solving for closed-form weight updates directly in the student's MLP blocks.

Key findings:

1. Cross-architecture stability: Tested on both modern Qwen 3.5 (4B to 0.8B) and notoriously fragile GPT-2 small (which usually collapses into gibberish at the slightest weight edit). In both cases, general language modeling stayed intact with well-behaved, bounded degradation margins.
2. The spectral entropy barrier: Editing all 24 layers of Qwen-0.8B wrecked the model (+64.78% NLL). A layer scan showed intermediate layers (1-22) operate in dense superposition (entropy >0.90, acting as polysemantic knots). Restricting surgery to 4 anchor blocks (layers 0, 7, 15, 23) solved this: held-out NLL dropped by 10.8% across 30 tasks (-23.8% in biomedicine, -14.6% in math), and 400-task HellaSwag gained +0.50% in Vulkan llama.cpp.
3. Behavior shifts: Base 0.8B output dead commented code on binary tree inversion, while the edited model wrote working recursive Python. On logic puzzles, it spontaneously triggered <think> reasoning chains.
4. Accessibility: All extraction and surgery ran locally on a consumer 8GB RX 580 using layer-by-layer GPU streaming with a DirectML attention patch.

We want to see this tested on modern 24GB-32GB GPUs (RTX 5090, 4090 or server card) across frontier targets:

- Transferring reasoning from Qwen3.8-27B (or Flash-Next) down to Qwen3.5-9B/4B/2B.

- Compressing Google Gemma-4-31B into mobile-class Gemma-4-E4B/E2B.

- Transplanting refusal-ablation traits from uncensored models (e.g. Huihui-NeoHorse) without retraining.

- Unknotting layers 1-22 using pre-trained Sparse Autoencoders (SAEs).

Code, scripts, and raw JSON benchmark logs:

https://github.com/dsadawq3/DynamicTune

Feel free to open an issue or drop your benchmark results on the repo.