Create README.md

README.md

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+---
+license: mit
+language:
+- en
+tags:
+- mlx
+- apple-silicon
+- multimodal
+- vision-language
+- pixtral
+- llava
+- quantized
+- 3bit
+- 4bit
+- 5bit
+- 6bit
+pipeline_tag: image-text-to-text
+library_name: mlx
+---
+
+# Apriel-1.5-15B-Thinker — **MLX 3-bit** (Apple Silicon)
+
+**Format:** MLX (Mac, Apple Silicon)  
+**Quantization:** **3-bit** (balanced footprint ↔ quality)  
+**Base:** ServiceNow-AI/Apriel-1.5-15B-Thinker  
+**Architecture:** Pixtral-style LLaVA (vision encoder → 2-layer projector → decoder)
+
+This repository provides a **3-bit MLX** build of Apriel-1.5-15B-Thinker for **on-device** multimodal inference on Apple Silicon. In side-by-side tests, the **3-bit** variant often:
+- uses **significantly less RAM** than 6-bit,
+- decodes **faster**, and
+- tends to produce **more direct answers** (less “thinking out loud”) at low temperature.
+
+If RAM allows, we also suggest trying **4-bit/5-bit/6-bit** variants (guidance below) for tasks that demand more fidelity.
+
+> Explore other Apriel MLX variants under the `mlx-community` namespace on the Hub.
+
+---
+
+## 🔎 Upstream → MLX summary
+
+Apriel-1.5-15B-Thinker is a multimodal reasoning VLM built via **depth upscaling**, **two-stage multimodal continual pretraining**, and **SFT with explicit reasoning traces** (math, coding, science, tool-use).  
+This MLX release converts the upstream checkpoint with **3-bit** quantization for smaller memory and quick startup on macOS.
+
+---
+
+## 📦 Contents
+
+- `config.json` (MLX config for Pixtral-style VLM)  
+- `mlx_model*.safetensors` (3-bit shards)  
+- `tokenizer.json`, `tokenizer_config.json`  
+- `processor_config.json` / `image_processor.json`  
+- `model_index.json` and metadata
+
+---
+
+## 🚀 Quickstart (CLI)
+
+**Single image caption**
+```bash
+python -m mlx_vlm.generate \
+  --model <this-repo-id> \
+  --image /path/to/image.jpg \
+  --prompt "Describe this image in two concise sentences." \
+  --max-tokens 128 --temperature 0.0 --device mps --seed 0