Instructions to use True2456/Qwen3.8-Flash-Next-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use True2456/Qwen3.8-Flash-Next-MLX-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("True2456/Qwen3.8-Flash-Next-MLX-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use True2456/Qwen3.8-Flash-Next-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "True2456/Qwen3.8-Flash-Next-MLX-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "True2456/Qwen3.8-Flash-Next-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use True2456/Qwen3.8-Flash-Next-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "True2456/Qwen3.8-Flash-Next-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "True2456/Qwen3.8-Flash-Next-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "True2456/Qwen3.8-Flash-Next-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use True2456/Qwen3.8-Flash-Next-MLX-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "True2456/Qwen3.8-Flash-Next-MLX-4bit"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default True2456/Qwen3.8-Flash-Next-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use True2456/Qwen3.8-Flash-Next-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "True2456/Qwen3.8-Flash-Next-MLX-4bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "True2456/Qwen3.8-Flash-Next-MLX-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.8-Flash-Next MLX 4-bit

Affine MLX quantization of Qwen/Qwen3.8-Flash-Next (qwen4_exp). This is the 4-bit bank the Rindi Flash-Next serve path uses for resident MoE experts, the lm_head, and the MTP drafter on Apple Silicon.

It is not a drop-in for stock mlx-lm until that package has qwen4_exp. It is not the ANE MIL graphs (those are M5/h17 compiled programs and will ship separately).

Files in this repo

path	role	size
`model.safetensors`	language weights (experts 4-bit gs64; GDN/attn/embed mostly 8-bit)	~71 GB
`mtp/mtp-4bit.safetensors`	MTP drafter	~1.4 GB
`model-indexer.safetensors`	QSA indexer	~41 MB
`vision/`	vision tower (unused by the text ANE server)	~856 MB
tokenizer + `chat_template.jinja`	Qwen chat template (`low` / `medium` / `xhigh`)	—
`ngram/`	PLE n-gram embedding table (unmodified official bf16 shards `00006`–`00036` plus `ngram-extra`)	~95 GB
`ngram_index.json`	PLE hash constants	—

The n-gram table is the official Qwen/Qwen3.8-Flash-Next PLE embeddings, not quantized. Without those files the runtime can still load with a zeros PLE fallback.

Recipe

Default 4-bit group size 64. Per-family overrides in config.json:

experts gate_up / down: 4-bit
GDN in/out proj, attention, embed, MTP: 8-bit
router and gated-residual (hyper): 16-bit

Converted on an M5 Max from the official BF16 checkpoint. Do not mix this file with conversions that folded Qwen4ExpRMSNorm's 1+w into the saved gains (those generate noise).

Requirements

Apple Silicon. The ANE hybrid path that consumes this bank is currently M5 (h17), ~128 GB unified memory.
A qwen4_exp MLX runtime (local mlx-lm / mlx-vlm checkout, not an old PyPI wheel).
Qwen Community License 1.0 (see LICENSE). Redistribution of this derivative is allowed; commercial MaaS / coding-assistant products have extra terms in that license.

Acknowledgements

Weights, architecture, and tokenizer: Qwen. Quantization and Apple Silicon packaging: this repository.

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Safetensors

Model size

128B params

Tensor type

U32

BF16

I64

MLX

Hardware compatibility

Quantized

Model tree for True2456/Qwen3.8-Flash-Next-MLX-4bit

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Qwen/Qwen3.8-Flash-Next

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