Instructions to use TensorFold/Qwen3.8-27B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TensorFold/Qwen3.8-27B-MLX-8bit with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("TensorFold/Qwen3.8-27B-MLX-8bit")
config = load_config("TensorFold/Qwen3.8-27B-MLX-8bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use TensorFold/Qwen3.8-27B-MLX-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TensorFold/Qwen3.8-27B-MLX-8bit"

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": "TensorFold/Qwen3.8-27B-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use TensorFold/Qwen3.8-27B-MLX-8bit 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 "TensorFold/Qwen3.8-27B-MLX-8bit"

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 TensorFold/Qwen3.8-27B-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TensorFold/Qwen3.8-27B-MLX-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TensorFold/Qwen3.8-27B-MLX-8bit"

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 "TensorFold/Qwen3.8-27B-MLX-8bit" \
  --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"

ashxhart commited on Aug 26

Commit

9ed12b4

verified ·

1 Parent(s): 0d508b6

Fix Qwen logo sizing

Browse files

Files changed (2) hide show

README.md +2 -2
qwen-logo.png +0 -0

README.md CHANGED Viewed

@@ -17,7 +17,7 @@ tags:
 ---
 <p align="center">
-  <a href="https://qwenlm.github.io/"><img src="https://qwenlm.github.io/img/logo.png" alt="Qwen" height="52"></a><br>
   <img src="https://img.shields.io/badge/Apple_Silicon-MLX-000000?style=for-the-badge&logo=apple&logoColor=white" alt="Apple silicon MLX">
   <img src="https://img.shields.io/badge/Vontra-oMLX-6E56CF?style=for-the-badge&logo=huggingface&logoColor=white" alt="Vontra oMLX">
 </p>
@@ -98,7 +98,7 @@ hf download Vontra/Qwen3.8-27B-MLX-8bit \
 3. Load `Qwen3.8-27B-MLX-8bit` and use the chat UI or OpenAI-compatible endpoint.
 ```bash
-curl http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -H "Authorization: Bearer $OMLX_API_KEY" \
   -d '{

 ---
 <p align="center">
+  <a href="https://qwenlm.github.io/"><img src="qwen-logo.png" width="96" height="95" alt="Qwen"></a><br>
   <img src="https://img.shields.io/badge/Apple_Silicon-MLX-000000?style=for-the-badge&logo=apple&logoColor=white" alt="Apple silicon MLX">
   <img src="https://img.shields.io/badge/Vontra-oMLX-6E56CF?style=for-the-badge&logo=huggingface&logoColor=white" alt="Vontra oMLX">
 </p>
 3. Load `Qwen3.8-27B-MLX-8bit` and use the chat UI or OpenAI-compatible endpoint.
 ```bash
+curl "$OMLX_BASE_URL/v1/chat/completions" \
   -H "Content-Type: application/json" \
   -H "Authorization: Bearer $OMLX_API_KEY" \
   -d '{

qwen-logo.png ADDED Viewed