---
license: mit
language:
- en
tags:
- mlx
- apple-silicon
- multimodal
- vision-language
- pixtral
- llava
- quantized
- 6bit
pipeline_tag: image-text-to-text
library_name: mlx
base_model:
- ServiceNow-AI/Apriel-1.5-15b-Thinker
---

# Apriel-1.5-15B-Thinker — MLX Quantized (Apple Silicon)

**Format:** MLX (Apple Silicon)  
**Variants:** 6-bit (recommended)
**Base model:** ServiceNow-AI/Apriel-1.5-15B-Thinker  
**Architecture:** Pixtral-style LLaVA (vision encoder → 2-layer projector → decoder)  
**Intended use:** image understanding & grounded reasoning; document/chart/OCR-style tasks; math/coding Q&A with visual context.

> This repository provides **MLX-format** weights for Apple Silicon (M-series) built from the original Apriel-1.5-15B-Thinker release. It is optimized for **on-device** inference with small memory footprints and fast startup on macOS.

---

## 🔎 What is Apriel-1.5-15B-Thinker?

Apriel-1.5-15B-Thinker is a 15B **open-weights multimodal reasoning** model trained via a data-centric **mid-training** recipe rather than RLHF/RM. Starting from **Pixtral-12B** as the base, the authors apply:
1) **Depth Upscaling** (capacity expansion without pretraining from scratch),  
2) **Two-stage multimodal continual pretraining (CPT)** to build text + visual reasoning, and  
3) **High-quality SFT** with explicit reasoning traces across math, coding, science, and tool use.  
This approach delivers **frontier-level** capability on compact compute. :contentReference[oaicite:0]{index=0}

### Key reported results (original model)
- **AAI Index**: 52, **matching DeepSeek-R1-0528** at far lower compute. :contentReference[oaicite:1]{index=1}
- **Multimodal**: On 10 image benchmarks, **within ~5 points** of Gemini-2.5-Flash and Claude Sonnet-3.7 on average. :contentReference[oaicite:2]{index=2}
- Designed for **single-GPU / constrained deployment** scenarios. :contentReference[oaicite:3]{index=3}

> Notes above summarize the upstream paper; MLX quantization can slightly affect absolute scores. Always validate on your use case.

---

## 🏗️ Architecture (high level)

- **Backbone:** Pixtral-12B-Base-2409 adapted to a larger 15B decoder via **depth upscaling** (layers 40 → 48), then re-aligned with a **2-layer projection network** connecting the vision encoder and decoder. :contentReference[oaicite:4]{index=4}
- **Training stack:**
  - **CPT Stage-1:** mixed tokens (≈50% text, 20% replay, 30% multimodal) for foundational reasoning & image understanding; 32k context; cosine LR with warmup; all components unfrozen; checkpoint averaging. :contentReference[oaicite:5]{index=5}
  - **CPT Stage-2:** targeted synthetic visual tasks (reconstruction, visual matching, detection, counting) to strengthen **spatial/compositional/fine-grained** reasoning; vision encoder frozen; loss on responses for instruct data; 16k context. :contentReference[oaicite:6]{index=6}
  - **SFT:** curated instruction-response pairs with **explicit reasoning traces** (math, coding, science, tools). :contentReference[oaicite:7]{index=7}

---

## 💾 This MLX Release

- **Why MLX?** Native Apple-Silicon inference with small binaries, fast load, and low memory overhead.
- **What’s included:** `config.json`, `mlx_model*.safetensors` (sharded), tokenizer & processor files, and metadata for VLM pipelines.
- **Quantization options:**
  - **6-bit (recommended):** best balance of quality & memory.

> Tip: If you’re capacity-constrained on an M1/M2, try **6-bit** first;

---

## ⚙️ Quickstart (CLI)

```bash
# Basic image caption
python -m mlx_vlm.generate \
  --model <this-repo-id> \
  --image /path/to/image.jpg \
  --prompt "Describe this image." \
  --max-tokens 128 --temperature 0.0 --device mps