Instructions to use jedisct1/Qwen3.8-Flash-Next-oQ4e-128k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jedisct1/Qwen3.8-Flash-Next-oQ4e-128k 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("jedisct1/Qwen3.8-Flash-Next-oQ4e-128k")

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 jedisct1/Qwen3.8-Flash-Next-oQ4e-128k with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jedisct1/Qwen3.8-Flash-Next-oQ4e-128k"

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": "jedisct1/Qwen3.8-Flash-Next-oQ4e-128k"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use jedisct1/Qwen3.8-Flash-Next-oQ4e-128k with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "jedisct1/Qwen3.8-Flash-Next-oQ4e-128k"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "jedisct1/Qwen3.8-Flash-Next-oQ4e-128k"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "jedisct1/Qwen3.8-Flash-Next-oQ4e-128k",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use jedisct1/Qwen3.8-Flash-Next-oQ4e-128k 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 "jedisct1/Qwen3.8-Flash-Next-oQ4e-128k"

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 jedisct1/Qwen3.8-Flash-Next-oQ4e-128k

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jedisct1/Qwen3.8-Flash-Next-oQ4e-128k with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "jedisct1/Qwen3.8-Flash-Next-oQ4e-128k"

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 "jedisct1/Qwen3.8-Flash-Next-oQ4e-128k" \
  --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-oQ4e-128k / omlx_support /serve

jedisct1

Add model card and configuration

2b5444a verified about 7 hours ago

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889 Bytes

	#!/bin/zsh
	set -euo pipefail

	support_root=${0:A:h}
	model_root=${support_root:h}
	models_root=${model_root:h}
	base_path=${OMLX_BASE_PATH:-$model_root/.omlx}
	cache_dir=${OMLX_CACHE_DIR:-$base_path/cache}
	api_key=${OMLX_API_KEY:-omlx}
	port=${OMLX_PORT:-8766}

	mkdir -p "$cache_dir"
	if [[ ! -f "$base_path/model_settings.json" ]]; then
	cp "$support_root/model_settings.json" "$base_path/model_settings.json"
	fi
	export OMLX_QWEN4_PLE_MODE=mmap
	export OMLX_QWEN4_PLE_MODEL_PATH="$model_root"

	exec /bin/zsh "$support_root/with-omlx-python" -m omlx.cli serve \
	--model-dir "$models_root" \
	--host 127.0.0.1 \
	--port "$port" \
	--max-concurrent-requests 1 \
	--memory-guard balanced \
	--paged-ssd-cache-dir "$cache_dir" \
	--paged-ssd-cache-max-size 128GB \
	--hot-cache-max-size 0 \
	--initial-cache-blocks 1 \
	--no-hf-cache \
	--base-path "$base_path" \
	--api-key "$api_key"