Occamy-1.0-6bit-XL-mlx

leonsarmiento/Occamy-1.0-6bit-XL-mlx

Occamy-1.0 by Accio-Lab — an agentic co-worker built on Qwen3.6-35B-A3B, quantized for Apple Silicon using the BaseQuant_XL 6/8-bit recipe.

Occamy-1.0 is purpose-built for long-horizon, stateful agent work: coordinated use of search, code, tools, files, structured APIs, and productivity software — with reliable execution, persistent state tracking, recovery, and follow-through. Best among 35B-A3B peers on most agentic benchmarks: 82.20 Claw-Eval avg (+12.7 over its Qwen3.6 base), 49.16 WildClawBench, 59.0 Terminal-Bench 2.1, 65.40 BFCL v4, 91.53 IFEval.

This is a full multimodal build — the vision tower is preserved. (Accio froze the vision encoder/projector during post-training, so vision behavior matches the Qwen3.6 base.)

About XL Quantization

BaseQuant_XL is a fully data-agnostic, static quantization. No calibration dataset, no sensitivity analysis, no importance matrix. Precision is allocated purely by architectural role — routing-critical layers get higher precision, bulk expert parameters get lower precision. The result is a transparent, faithful capture of the source model.

Data-dependent calibration quantizations (iMatrix, AWQ, GPTQ, oQ, oQ4e, etc.) use a calibration set to guide bit allocation. This can produce a skewed representation of the model: domains well-represented in the calibration data (English, popular topics, public or leaked benchmarks) are preserved better, while underrepresented domains (non-English languages, niche use cases, your own data) are preserved worse. XL avoids this trade-off entirely — it generalizes honestly because it is never fit to any particular data distribution.

Quickstart

pip install -U mlx-vlm

python -m mlx_vlm.generate --model leonsarmiento/Occamy-1.0-6bit-XL-mlx --max-tokens 4096 --temperature 1.0 --top-p 0.95 --prompt "Search the repo for where errors from the payment API are swallowed, then propose a fix."

Works with LM Studio — vision mmproj included. Thinking mode is on by default (emits <think>...</think>).

Quantization Strategy

BaseQuant_XL recipe — precision is allocated by layer importance, not applied uniformly:

• Bits per weight: 6.808 · Total size: 29.9 GB · Group size: 64

Notes specific to this build:

• Source stores routed experts as individual per-expert tensors (experts.N.{gate,up,down}_proj, N = 0–255) — stock mlx_vlm 0.7.0 sanitize stacks them into switch_mlp 3D tensors at load; no custom sanitize needed.
• Source ships the vision tower as a separate model-visual.safetensors (transformers 5.x split layout) — picked up natively by the loader.
• No MTP layer in the source (mtp_num_hidden_layers: 0).

Recommended Inference Parameters

Per the source model card:

Thinking is on by default (<think>...</think> before the answer); with a reasoning parser enabled the chain-of-thought is returned in a separate reasoning_content field. Context: 262,144 native (SFT-trained at 131,072).

Model Overview

Source Model Benchmarks (from Accio-Lab)

Trained openly via Accio's Dressage framework: full-parameter SFT (~15,000 trajectories / 403.3M tokens), HDPO, a two-expert merge (Marathon for sustained execution + Sprint for broad agentic capability), and SAO. Weights, dataset (occamy-data-1.0), and training code are all released.

Source

Accio-Lab/occamy-1.0