Apodex-1.1-mini-6bit-XL-mlx

leonsarmiento/Apodex-1.1-mini-6bit-XL-mlx

Apodex-1.1-mini by Apodex AI — a reasoning-first agent model for complex, long-horizon research tasks (Qwen/Qwen3.5-35B-A3B fine-tune), quantized for Apple Silicon using the BaseQuant_XL 6/8-bit recipe.

Only ~3B parameters active per token. Apodex works directly with papers, datasets, spreadsheets, images, and code — cleaning data, selecting methods, running analyses, recovering from errors, and turning raw inputs into verifiable deliverables within one continuous task.

This is a full multimodal build — the base model's vision tower is preserved (the fine-tune itself is text-trained; the tower carries over from Qwen3.5-35B-A3B).

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/Apodex-1.1-mini-6bit-XL-mlx --max-tokens 4096 --temperature 1.0 --top-p 0.95 --prompt "Research the current state of solid-state battery manufacturing and produce a short brief."

Apodex follows the Qwen3.5 chat template — tool calls are emitted as <tool_call><function=...><parameter=...></parameter></function></tool_call> and reasoning as <think>...</think>. Launch with the matching parsers so the server returns standard OpenAI-style tool_calls and reasoning_content fields.

Quantization Strategy

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

• Bits per weight: 6.808 · Total size: 29.87 GB (6 shards) · Group size: 64

Notes specific to this build:

• Source ships an MTP (multi-token prediction) layer (785 tensors) — dropped in this build; the MTP config declaration is stripped so engines don't flag a phantom MTP.
• Source stores merged per-layer expert tensors — converts via stock mlx_vlm sanitize (no custom sanitize needed).

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.

Agentic Usage

Apodex is trained for native function calling — pass tool schemas via the tools= parameter of the chat-completions API (rendered into the prompt by the chat template) rather than inlining them in the system prompt, so the qwen3_coder parser can recover structured tool_calls. The system prompt only needs the role and the high-level objective. Source-recommended harness: ApodexAI/FrontierAgent.

Model Overview

Source Model Benchmarks (from Apodex AI)

Apodex-1.1-mini with the Agent Team harness remains competitive with frontier models — leading FrontierFinance at 50.2 and nearly matching the best result on APEX-Agents at 27.7. The Agent Team setup consistently outperforms a ReAct loop across all reported benchmarks.

The full Apodex-1.1 Agent Team reports 38.5 APEX-Agents, 78.8 GDPVal, 54.3 FrontierFinance, 63.3 FrontierScience-Research, 35.3 BioMysteryBench, 56.1 Humanity's Last Exam.

Source

apodex/Apodex-1.1-mini — Apodex 1.1 tech blog