--- license: mit pipeline_tag: text-generation library_name: mlx base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B base_model_relation: quantized quantized_by: Outlier-Ai tags: - 4-bit - 4bit - apple-silicon - chain-of-thought - chat - conversational - deepseek - deepseek-r1 - deepseek-r1-distill - edge-ai - function-calling - instruct - local-llm - m1 - m2 - m3 - m4 - mac - mac-mini - mac-studio - macbook-air - macbook-pro - macos - metal - mlx - mlx-community - mlx-lm - no-cloud - offline - on-device - outlier - outlier-app - private - quantized - r1 - r1-distill - reasoning - safetensors - text-generation - thinking language: - en - zh - fr - es - pt - de - ru - ja - ko - ar widget: - example_title: Widget math messages: - role: user content: If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets? Show reasoning. - example_title: Logic grid messages: - role: user content: Alice, Bob, and Carol each own a different pet. Alice does not own the dog. Bob owns the parrot. Who owns what? - example_title: Word problem messages: - role: user content: A train leaves station A at 60 mph and another leaves station B, 180 miles away, at 40 mph toward each other. When do they meet? --- > **Run this on your Mac with [Outlier](https://outlier.host/?utm_source=hf&utm_medium=modelcard&utm_campaign=deepseek_r1_distill_qwen_32b_mlx_4bit)** — a one-click app that loads MLX models locally. macOS arm64, free download. # DeepSeek-R1-Distill-Qwen-32B (MLX 4-bit) MLX 4-bit conversion of [`deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B), repackaged for Apple Silicon. Original weights, original license — see frontmatter above. This repo only changes the on-disk format (safetensors, MLX 4-bit, `chat_template.jinja`, tokenizer). ## About this conversion - **Format:** MLX 4-bit safetensors (group size 64, symmetric) - **Tooling:** `mlx-lm` 0.31.x compatible - **Files:** `model.safetensors` shards · `config.json` · tokenizer · `chat_template.jinja` - **License:** inherits from the upstream base model — see YAML `license` field ### Load directly with `mlx-lm` ```bash pip install mlx-lm python -m mlx_lm.generate \ --model Outlier-Ai/DeepSeek-R1-Distill-Qwen-32B-MLX-4bit \ --prompt "Hello, world." \ --max-tokens 256 ``` Or in Python: ```python from mlx_lm import load, generate model, tokenizer = load("Outlier-Ai/DeepSeek-R1-Distill-Qwen-32B-MLX-4bit") print(generate(model, tokenizer, prompt="Hello, world.", max_tokens=256)) ``` ## What is Outlier? Outlier is a free macOS app that runs language models on your Mac, fully offline. Pick a model from a tier picker, click download, and chat — no API keys, no cloud round-trips, no usage caps. It ships with its own curated tier of MLX-4bit models and can also load any compatible MLX conversion (including this one) via the model picker. ➡ Download Outlier (free, Apple Silicon): **[outlier.host](https://outlier.host/?utm_source=hf&utm_medium=modelcard&utm_campaign=deepseek_r1_distill_qwen_32b_mlx_4bit)** For benchmark numbers (MMLU, HumanEval, tok/s on M-series Macs) with full provenance, see [outlier.host/benchmarks](https://outlier.host/benchmarks?utm_source=hf&utm_medium=modelcard&utm_campaign=deepseek_r1_distill_qwen_32b_mlx_4bit). ## Other Outlier conversions - [DeepSeek-R1-Distill-Qwen-7B (MLX 4-bit) — MLX 4-bit conversion (1,932 downloads)](https://huggingface.co/Outlier-Ai/DeepSeek-R1-Distill-Qwen-7B-MLX-4bit) - [DeepSeek-R1-Distill-Llama-8B (MLX 4-bit) — MLX 4-bit conversion (1,148 downloads)](https://huggingface.co/Outlier-Ai/DeepSeek-R1-Distill-Llama-8B-MLX-4bit) - [DeepSeek-R1-Distill-Qwen-14B (MLX 4-bit) — MLX 4-bit conversion (1,087 downloads)](https://huggingface.co/Outlier-Ai/DeepSeek-R1-Distill-Qwen-14B-MLX-4bit) - [Outlier-Core-27B (MLX 4-bit) — MLX 4-bit conversion (55 downloads)](https://huggingface.co/Outlier-Ai/Outlier-Core-27B-MLX-4bit) - [Outlier-Nano-4B (MLX 4-bit) — MLX 4-bit conversion (67 downloads)](https://huggingface.co/Outlier-Ai/Outlier-Nano-4B-MLX-4bit) ## License This conversion preserves the upstream license declared in the frontmatter (`mit`). Refer to the upstream base model card for the canonical license text and any usage restrictions.