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

Libraries

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

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use EigenLabs/Qwen3.8-27B-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 "EigenLabs/Qwen3.8-27B-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 "EigenLabs/Qwen3.8-27B-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"

gajesh commited on 19 days ago

Commit

301e9e2

verified ·

1 Parent(s): bbeabc6

Correct model card metadata, modality wording, and mlx-vlm command

Browse files

Files changed (1) hide show

README.md +2 -3

README.md CHANGED Viewed

@@ -4,7 +4,6 @@ license: apache-2.0
 pipeline_tag: image-text-to-text
 base_model:
 - Qwen/Qwen3.8-27B
-- EigenLabs/Qwen3.8-27B-4bit
 tags:
 - mlx
 - mlx-vlm
@@ -13,7 +12,7 @@ tags:
 # EigenLabs/Qwen3.8-27B-4bit
-This revision is a complete MLX 4-bit Qwen3.8 vision-language model for image, video, and text generation.
 The **1,847 `language_model.*` tensors are bit-for-bit identical** to [`EigenLabs/Qwen3.8-27B-4bit@eda45ab47f465d08d6558f0353a2346e2eb9d5b3`](https://huggingface.co/EigenLabs/Qwen3.8-27B-4bit/tree/eda45ab47f465d08d6558f0353a2346e2eb9d5b3). The 333 `vision_tower.*` tensors and multimodal processor assets come from [`mlx-community/Qwen3.8-27B-4bit@3e6447f082e89cc7f0bc6e5441afd38dfce760ff`](https://huggingface.co/mlx-community/Qwen3.8-27B-4bit/tree/3e6447f082e89cc7f0bc6e5441afd38dfce760ff), converted from the official [`Qwen/Qwen3.8-27B@1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0`](https://huggingface.co/Qwen/Qwen3.8-27B/tree/1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0) source. Tensor dtype, shape, and raw bytes were checked before publication.
@@ -23,7 +22,7 @@ The immutable challenge revision [`eda45ab47f465d08d6558f0353a2346e2eb9d5b3`](ht
 ```bash
 pip install -U mlx-vlm
-mlx_vlm generate \
   --model EigenLabs/Qwen3.8-27B-4bit \
   --image /path/to/image.jpg \
   --prompt "Describe this image." \

 pipeline_tag: image-text-to-text
 base_model:
 - Qwen/Qwen3.8-27B
 tags:
 - mlx
 - mlx-vlm
 # EigenLabs/Qwen3.8-27B-4bit
+This revision is a complete MLX 4-bit Qwen3.8 vision-language model that accepts image, video, and text inputs and generates text.
 The **1,847 `language_model.*` tensors are bit-for-bit identical** to [`EigenLabs/Qwen3.8-27B-4bit@eda45ab47f465d08d6558f0353a2346e2eb9d5b3`](https://huggingface.co/EigenLabs/Qwen3.8-27B-4bit/tree/eda45ab47f465d08d6558f0353a2346e2eb9d5b3). The 333 `vision_tower.*` tensors and multimodal processor assets come from [`mlx-community/Qwen3.8-27B-4bit@3e6447f082e89cc7f0bc6e5441afd38dfce760ff`](https://huggingface.co/mlx-community/Qwen3.8-27B-4bit/tree/3e6447f082e89cc7f0bc6e5441afd38dfce760ff), converted from the official [`Qwen/Qwen3.8-27B@1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0`](https://huggingface.co/Qwen/Qwen3.8-27B/tree/1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0) source. Tensor dtype, shape, and raw bytes were checked before publication.
 ```bash
 pip install -U mlx-vlm
+mlx_vlm.generate \
   --model EigenLabs/Qwen3.8-27B-4bit \
   --image /path/to/image.jpg \
   --prompt "Describe this image." \