Instructions to use True2456/Qwen3.8-Flash-Next-MLX-4bit-ablation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use True2456/Qwen3.8-Flash-Next-MLX-4bit-ablation 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-ablation") 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
- Pi
How to use True2456/Qwen3.8-Flash-Next-MLX-4bit-ablation 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-ablation"
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-ablation" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use True2456/Qwen3.8-Flash-Next-MLX-4bit-ablation 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-ablation"
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-ablation" # 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-ablation", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use True2456/Qwen3.8-Flash-Next-MLX-4bit-ablation 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-ablation"
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-ablation
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use True2456/Qwen3.8-Flash-Next-MLX-4bit-ablation 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-ablation"
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-ablation" \ --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 (with refusal directions)
This repository is identical to True2456/Qwen3.8-Flash-Next-MLX-4bit, with the same weights byte for byte, plus one extra file: refusal_directions.safetensors. It holds one direction per decoder layer, which Rindi-Agent's Refusal ablation setting projects out of the residual stream at inference time.
- The weights are not modified. At 0, the default, the model behaves exactly like the stock one, and the setting can be changed or turned off at any time.
- Where to set it: Rindi-Agent โบ Settings โบ General โบ Refusal ablation. 0 is the stock model, around 1โ2 loosens over-eager refusals, and higher values remove refusals broadly at a cost in coherence. It is meant for security and red-team lab work.
- Other runtimes ignore the file.
If you don't need the setting, use the original repository.
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) |
โ |
ngram/ |
PLE n-gram embedding table (unmodified official bf16 shards 00006โ00036 plus ngram-extra) |
~95 GB |
ngram_index.json |
PLE hash constants | โ |
refusal_directions.safetensors |
per-layer refusal directions for Rindi-Agent's Refusal ablation setting ([layers, hidden]) |
~0.5 MB |
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_expMLX 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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