Instructions to use agnosticeng/Qwen3.8-Flash-Next-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use agnosticeng/Qwen3.8-Flash-Next-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("agnosticeng/Qwen3.8-Flash-Next-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
- Pi
How to use agnosticeng/Qwen3.8-Flash-Next-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 "agnosticeng/Qwen3.8-Flash-Next-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": "agnosticeng/Qwen3.8-Flash-Next-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use agnosticeng/Qwen3.8-Flash-Next-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 "agnosticeng/Qwen3.8-Flash-Next-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "agnosticeng/Qwen3.8-Flash-Next-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "agnosticeng/Qwen3.8-Flash-Next-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use agnosticeng/Qwen3.8-Flash-Next-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 "agnosticeng/Qwen3.8-Flash-Next-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 agnosticeng/Qwen3.8-Flash-Next-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use agnosticeng/Qwen3.8-Flash-Next-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 "agnosticeng/Qwen3.8-Flash-Next-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 "agnosticeng/Qwen3.8-Flash-Next-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 4-bit (MLX affine)
4-bit quantized build of the text tower of
Qwen/Qwen3.8-Flash-Next
(qwen4_exp), packed for the MLX-style runtime used by
lisa.
Quantization
- Affine, 4-bit (MLX
mx.quantize): packedu32weights + onebf16scale/bias pair per group of weights along the input dim. - Group size 32. Groups of 32 weights share one scale/bias pair.
- Group 32 is required by lisa's fused Flash-Next decode kernels, which hardcode
GS=32.
Smaller groups = more scale/bias overhead, lower quantization error. The group size is fixed by the checkpoint and must be honored when loading.
Contents
config.json qwen4_exp, language_model_only, quantization {group_size 32, bits 4}
model-00001.safetensors … trunk (17 shards, ~4.9 GB each)
mtp/model.safetensors multi-token-prediction draft head
ngram.safetensors merged n-gram table (4-bit gs32)
tokenizer.json … tokenizer
Conversion
Built from the public bf16 original with convert_qwen38_flash_next.py --gs 32
— streams the ~360 GB checkpoint shard by shard (download → quantize → delete).
Renames/transforms: language_model.model.* prefix, MoE switch_mlp repack,
conv1d [C,1,K] → [C,K,1], RMSNorm +1 fold.
- Downloads last month
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4-bit
Model tree for agnosticeng/Qwen3.8-Flash-Next-4bit
Base model
Qwen/Qwen3.8-Flash-Next