Instructions to use programmer-666/Qwen3-Coder-Next-oQ8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use programmer-666/Qwen3-Coder-Next-oQ8e 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("programmer-666/Qwen3-Coder-Next-oQ8e")

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 programmer-666/Qwen3-Coder-Next-oQ8e with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "programmer-666/Qwen3-Coder-Next-oQ8e"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "programmer-666/Qwen3-Coder-Next-oQ8e"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use programmer-666/Qwen3-Coder-Next-oQ8e 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 "programmer-666/Qwen3-Coder-Next-oQ8e"

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 programmer-666/Qwen3-Coder-Next-oQ8e

Run Hermes

hermes

OpenClaw new

How to use programmer-666/Qwen3-Coder-Next-oQ8e with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "programmer-666/Qwen3-Coder-Next-oQ8e"

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 "programmer-666/Qwen3-Coder-Next-oQ8e" \
  --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"

MLX LM

How to use programmer-666/Qwen3-Coder-Next-oQ8e with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "programmer-666/Qwen3-Coder-Next-oQ8e"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "programmer-666/Qwen3-Coder-Next-oQ8e"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "programmer-666/Qwen3-Coder-Next-oQ8e",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

programmer-666 commited on 6 days ago

Commit

a00a930

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1 Parent(s): edc542a

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@@ -48,7 +48,7 @@ The model can also be served through oMLX's OpenAI-compatible API endpoint for u
 ## Benchmarks
-All benchmarks were run with oMLX. Four variants were tested: this model (**oQ8e**) and an oQ2.7e quant, each in a default and an "Adjusted" configuration.
 > The "Adjusted" columns were run with tuned oMLX serving parameters (context window, sampling, and related runtime settings), rather than the server's default configuration.

 ## Benchmarks
+All benchmarks were run with oMLX. Four variants were tested: this model (**oQ8e**) and an oQ2.7e quant, each in a default and an "Adjusted" configuration. Tests were performed on a MacBook Pro M4 Max ARM processor and 128 GB of memory.
 > The "Adjusted" columns were run with tuned oMLX serving parameters (context window, sampling, and related runtime settings), rather than the server's default configuration.