Salience-1.5-Pro-6bit-XL-mlx

leonsarmiento/Salience-1.5-Pro-6bit-XL-mlx

Salience 1.5 Pro by Vection Labs — a 35B-A3B sparse MoE software-engineering agent with native vision, quantized for Apple Silicon using the BaseQuant_XL 6/8-bit recipe.

Only ~3B parameters active per token — the decode speed of a small model with the capacity of a 35B one. Built for hard engineering work: writing and debugging real code, repo-scale edits, robust backend systems, and driving tools through long-horizon tasks.

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/Salience-1.5-Pro-6bit-XL-mlx --max-tokens 256 --temperature 0.6 --top-p 0.95 --prompt "Implement an LRU cache in Python with O(1) get/put."

Works with LM Studio — vision mmproj included. Thinking mode is on by default (emits <think>...</think>). Pass enable_thinking=False for direct answers.

Quantization Strategy

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

• Bits per weight: ~6.8 · Total size: ~28 GB · Group size: 64

Recommended Inference Parameters

Lower temperature for precise code edits; higher for long agentic sessions.

Model Overview

MLX Benchmarks (n=50, instruct mode)

Salience leads Qwen3.6 on every benchmark and is competitive with Gemma-4 — +14 on MMLU_PRO reflects strong structured reasoning from its software-engineering tuning. Gemma-4 remains the coding champion (HUMANEVAL 98%, MBPP 82%).

MATHQA Thinking Efficiency (n=10, seed=42, 8192-token budget)

Reasoning efficiency measures how concisely a model arrives at the correct answer — fewer reasoning chars per correct answer means less context window consumed and less energy per answer. All three models achieve 10/10 verified accuracy; the differentiator is how much thinking it takes to get there.

Salience's SWE-focused tuning produces thorough, methodical reasoning chains — longer than Gemma-4's concise style, but every chain reaches the correct answer. Gemma-4 remains the reasoning-efficiency champion; Salience trades more thinking for broader engineering depth and multimodal capability.

Capabilities

• Code & SWE execution — runnable code, repo-scale edits, methodical debugging, robust backends
• Agentic tool-use — multi-step planning, tool orchestration, long-horizon task execution
• Deep reasoning — structured, inspectable chains of thought with one-token switch to disable
• Multimodal perception — diagrams, screenshots, documents, video as first-class inputs

Source Model

Made by Vection Labs. Base architecture: Qwen3.5 MoE (35B-A3B). MLX quantization by leonsarmiento.