Text Generation
MLX
Safetensors
qwen4_exp
omlx
oq
quantized
apple-silicon
mixture-of-experts
4-bit precision
long-context
conversational
tool-calling
speculative-decoding
mtp
Instructions to use jedisct1/Qwen3.8-Flash-Next-oQ4e-MTP-128k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jedisct1/Qwen3.8-Flash-Next-oQ4e-MTP-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-MTP-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
- Pi
How to use jedisct1/Qwen3.8-Flash-Next-oQ4e-MTP-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-MTP-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-MTP-128k" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use jedisct1/Qwen3.8-Flash-Next-oQ4e-MTP-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-MTP-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-MTP-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-MTP-128k", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use jedisct1/Qwen3.8-Flash-Next-oQ4e-MTP-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-MTP-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-MTP-128k
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use jedisct1/Qwen3.8-Flash-Next-oQ4e-MTP-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-MTP-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-MTP-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"
Send medium reasoning effort through extra body
Browse files
README.md
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repetition_penalty: 1.0
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```
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For multi-turn agents, preserve `reasoning_content` between tool turns and keep `preserve_thinking` enabled. Use `reasoning_effort = "medium"` as the general-purpose default. It retains native thinking without the extra system instruction added by the `xhigh` template tier. Reserve `xhigh` for unusually difficult tasks where the additional latency and reasoning tokens are justified.
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## Swival setup
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max_output_tokens = 32768
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temperature = 1.0
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top_p = 0.95
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reasoning_effort = "medium"
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extra_body = { top_k = 20, min_p = 0.0, presence_penalty = 0.0, repetition_penalty = 1.0, chat_template_kwargs = { enable_thinking = true, preserve_thinking = true } }
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```
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Run a task from its workspace so Swival can apply the intended filesystem and command boundaries:
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```sh
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repetition_penalty: 1.0
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```
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For multi-turn agents, preserve `reasoning_content` between tool turns and keep `preserve_thinking` enabled. Use `reasoning_effort = "medium"` inside `extra_body` as the general-purpose default. It retains native thinking without the extra system instruction added by the `xhigh` template tier. It is a prompt policy, not a hard reasoning-token budget. Reserve `xhigh` for unusually difficult tasks where the additional latency and reasoning tokens are justified.
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## Swival setup
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max_output_tokens = 32768
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temperature = 1.0
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top_p = 0.95
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extra_body = { reasoning_effort = "medium", top_k = 20, min_p = 0.0, presence_penalty = 0.0, repetition_penalty = 1.0, chat_template_kwargs = { enable_thinking = true, preserve_thinking = true } }
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```
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For this unregistered local model, Swival 1.0.41's top-level `reasoning_effort` is removed by LiteLLM's unsupported-parameter filter. Keeping the value inside `extra_body` sends the dedicated OpenAI-compatible field to oMLX. The field is named `reasoning_effort`; `thinking_effort` is not supported by this stack.
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Run a task from its workspace so Swival can apply the intended filesystem and command boundaries:
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```sh
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