Instructions to use lesa80/Qwen3.8-27B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lesa80/Qwen3.8-27B-MLX-4bit 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("lesa80/Qwen3.8-27B-MLX-4bit")
config = load_config("lesa80/Qwen3.8-27B-MLX-4bit")

# 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 lesa80/Qwen3.8-27B-MLX-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 "lesa80/Qwen3.8-27B-MLX-4bit"

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": "lesa80/Qwen3.8-27B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use lesa80/Qwen3.8-27B-MLX-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 "lesa80/Qwen3.8-27B-MLX-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 "lesa80/Qwen3.8-27B-MLX-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"

Hermes Agent

How to use lesa80/Qwen3.8-27B-MLX-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 "lesa80/Qwen3.8-27B-MLX-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 lesa80/Qwen3.8-27B-MLX-4bit

Run Hermes

hermes

Atomic Chat

Qwen3.8-27B-MLX-4bit

MLX 4-bit quantized version of Qwen/Qwen3.8-27B.

BF16 version: lesa80/Qwen3.8-27B-MLX-bf16

All capabilities preserved:

✅ Vision — image + video (27-layer ViT encoder)
✅ Reasoning — thinking mode (токены thinking / response)
✅ Длинный контекст — 262K токенов
✅ Multilingual — русский, английский, китайский

Property	Value
Quantization	4-bit affine, group_size=64
Model size	~16 GB
RAM required	16-24 GB
License	Apache 2.0

Usage

hf download lesa80/Qwen3.8-27B-MLX-4bit --local-dir ./qwen-mlx --local-dir-use-symlinks False

from mlx_vlm import load, generate
model, processor = load("lesa80/Qwen3.8-27B-MLX-4bit")
output = generate(model, processor, "Describe this image.", image=["image.jpg"])
print(output)

mlx_vlm.generate --model lesa80/Qwen3.8-27B-MLX-4bit --prompt "Hello" --max-tokens 200

Model tree for lesa80/Qwen3.8-27B-MLX-4bit

Base model

Qwen/Qwen3.8-27B

Quantized

(448)

this model

lesa80
/

Qwen3.8-27B-MLX-4bit

Qwen3.8-27B-MLX-4bit

Usage

Links

Model tree for lesa80/Qwen3.8-27B-MLX-4bit