Tested on M1 Max 64GB ComfyUI: Anima-Base v1.0 matches preview3-base in speed; WAI-Anima kana LoRA hits 22% on light prompts but 67% with hood+robe+embroidery added.
A hands-on log of running Qwen-Scope's Sparse Autoencoder locally on M1 Max 64GB with Qwen3-8B-Base, extracting feature IDs that discriminate between Japanese, English, code, and Chinese from a single middle layer.
The Qwen team released Qwen-Scope, a Sparse Autoencoder suite for Qwen3/Qwen3.5. 14 groups of SAEs covering inference-time steering, evaluation analysis, toxicity classification, data synthesis, and training improvement.
A verification log for converting color anime-style AI illustrations to manga-style monochrome. AI re-generation approaches lean to either color leakage or face drift, and pure deterministic local processing looks mechanical. Frames the next directions to try: putting a grayscale-only LoRA on Anima, and using See-through for part decomposition before mechanical composition.
With v3 captions kept as-is and only the training amount pushed up to Anima's official 12,000+ step recommendation, the direction hit rate went 100% at ep150-180, crashed to 0% at ep200, then partially recovered to 67% at ep227 — a non-monotonic curve. 600-720 exposures per training image is the sweet spot; over 800 triggers catastrophic forgetting. Learning rate 2e-5, ~11 hours / $10 of RunPod training plus a sweet-spot epoch scan.
Records of rewriting captions for the 53 training images for the WAI-Anima character LoRA retrain after side ponytail direction control failed last time. Wrote position information into natural language so Qwen3 TE could pick it up, and dropped the IL-era strategy of absorbing the entire hairstyle into the single 'kanachan' trigger by promoting hairstyle to independent Danbooru tags. Includes notes on year tag necessity, the bikini/nude swapped-caption discovery, and blazer color recognition drift.
Took 53 cleaned images prepared for WAI-IL and trained a WAI-Anima character LoRA with AnimaLoraToolkit + RunPod. Training itself ran for $1.22, but at inference the side ponytail direction wouldn't shift with Danbooru tags or natural language. Verification record showing the issue is a directional bias inherited from Anima base (preview3-base onward).
The NotebookLM clone open-notebook assumes Docker and cloud APIs by default. I installed SurrealDB natively, ran four processes in tmux, and wired everything through Ollama's qwen3.6:35b and bge-m3. I fed it the Qwen3.6 benchmark article I wrote this morning, and it answered with the correct numbers.
Tried Qwen3.6-27B on both Ollama and MLX. Ollama couldn't load the VL-projector-embedded GGUF, MLX ran it at 11 tok/s. On the side, running 35B-A3B under MLX was roughly 2× faster than the Ollama GGUF. Also had both models build a BBS to gauge intent handling.
A hands-on log of Qwen3.6-35B-A3B under Ollama 0.20.6. Generation speed matches Qwen3.5 at 27 tok/s, but thinking tokens grew 13× for the same prompt. Multi-turn, persona, and a three-tier NSFW probe are included.
