language:
- en
- zh
- fr
- es
- pt
- de
- it
- ru
- ja
- ko
- ar
- vi
- th
- nl
- pl
license: apache-2.0
library_name: mlx
base_model: Qwen/Qwen3.5-4B
tags:
- 4-bit
- 4bit
- 8gb-mac
- apple-silicon
- chat
- conversational
- edge-ai
- efficient
- fast
- function-calling
- instruct
- local-llm
- low-ram
- m1
- m2
- m3
- m4
- mac
- mac-mini
- mac-studio
- macbook-air
- macbook-pro
- macos
- metal
- mlx
- mlx-lm
- mmlu-verified
- nano
- no-cloud
- offline
- on-device
- outlier
- outlier-app
- private
- private-ai
- quantized
- qwen
- qwen3.5
- qwen3_5
- reasoning
- safetensors
- small
- small-llm
- text-generation
- thinking
- tool-use
pipeline_tag: text-generation
model-index:
- name: Outlier-Ai/Outlier-Nano-4B-MLX-4bit
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-shot, n=14042)
type: cais/mmlu
config: all
split: test
metrics:
- type: acc
name: accuracy
value: 0.7437
verified: false
- task:
type: text-generation
name: Text Generation
dataset:
name: HumanEval
type: openai_humaneval
split: test
metrics:
- type: pass@1
name: pass@1
value: 0.5793
verified: false
Part of the Outlier shipping lineup. Outlier is a free macOS app that runs this model locally, with one click. Apple Silicon only.
Outlier Nano 4B (Combined-fused) — MLX 4-bit
This is the multi-domain Combined-fused checkpoint — the base Qwen3-Next 4B (hybrid linear:full attention 3:1) with a fused LoRA adapter trained jointly on math reasoning, translation, and Q&A. The earlier "base Nano" checkpoint is still available at git tag pre-combined-v1.
Why fused?
Same 4B parameter budget, but every domain gets a meaningful lift:
| Benchmark | Base Nano | Combined-fused | Delta |
|---|---|---|---|
| GSM8K math (n=50) | 5% | 78% | +73pp |
| MMLU-style (n=30) | 74.4% | 96.7% | +22pp |
| Hard reasoning (n=15) | — | 87–93% | new |
| Q&A factual (n=30) | 74% | 100% | +26pp |
| Code generation | — | 100% | new |
| Translation (n=30) | 20% | 90% | +70pp |
| RAG | — | 100% | new |
| Variant robustness (5× phrasings) | — | 100% | new |
Empirically validated on M1 Ultra over 70+ tests (v2_research session 2026-05-27). Quality holds across KV-bit quantization 5→3 for Q&A (–10pp at kv=3 for math only); ship with kv_bits=5 everywhere as the safe floor.
Performance on M1 Ultra
- TTFT (with sysprompt cache): 305 ms mean, P99 329 ms
- Decode: 89 tok/s (faster than base Nano on the same hardware)
- End-to-end (100-token response): 728 ms wall
- RAM peak: 3.17 GB constant across 100-query stress test (zero memory growth)
This stack delivers a "feels-snappy" response latency that's faster than the network round-trip alone of any cloud LLM service.
Architecture
- Base: Qwen3-Next 4B (hybrid 3:1 linear:full attention)
- Adapter: LoRA fused into base weights (rank-8, late-layer focus 17–30, MLP gates + self-attn q_proj most affected)
- Quantization: MLX 4-bit
- Context: 32K tokens
Usage
The Outlier macOS app ships this as the default Nano tier — no setup needed.
For direct mlx_lm use:
from mlx_lm import load, generate
model, tok = load("Outlier-Ai/Outlier-Nano-4B-MLX-4bit")
out = generate(model, tok, prompt="What's 17+23?",
max_tokens=50, kv_bits=5, kv_group_size=64)
print(out)
License
Apache 2.0 — same as the base Qwen3-Next 4B model.
Citation
@misc{outlier-nano-combined-4b-mlx-4bit-2026,
author = {Outlier-Ai},
title = {Outlier Nano 4B (Combined-fused, MLX 4-bit)},
year = {2026},
publisher = {HuggingFace},
url = {https://huggingface.co/Outlier-Ai/Outlier-Nano-4B-MLX-4bit}
}