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

Libraries

How to use netprojects/Qwen-Concept-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("netprojects/Qwen-Concept-27B-MLX-8bit")
config = load_config("netprojects/Qwen-Concept-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 netprojects/Qwen-Concept-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 "netprojects/Qwen-Concept-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": "netprojects/Qwen-Concept-27B-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use netprojects/Qwen-Concept-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 "netprojects/Qwen-Concept-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 netprojects/Qwen-Concept-27B-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use netprojects/Qwen-Concept-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 "netprojects/Qwen-Concept-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 "netprojects/Qwen-Concept-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"

Qwen Concept 27B for Apple Silicon (MLX, 8-bit)

An MLX conversion of ConceptCountry/qwen-concept-27b (revision 4c96373, bf16 weights). Qwen Concept 27B is Qwen3.8-27B with nine MLP down-projections edited by Concept Country so that the model reaches its answer with less thinking (paper). This build quantises the language model to 8-bit (affine, group 64) and keeps the vision tower in bf16, so it runs in mlx-lm (text) and mlx-vlm (text and images). Size: 29.5 GB.

Published by NetProjects (Internet Projects Ltd). Not affiliated with Concept Country or Alibaba Cloud.

Build	Size	Same next token as bf16
netprojects/Qwen-Concept-27B-MLX-6bit (recommended)	22.8 GB	98.6%
netprojects/Qwen-Concept-27B-MLX-8bit	29.5 GB	99.0%
netprojects/Qwen-Concept-27B-MLX-4bit	16.1 GB	95.3%
netprojects/Qwen-Concept-27B-MLX-bf16	54.7 GB	reference

The shorter thinking holds after quantisation

Hard math problems (MATH-500, level 5) with thinking on and greedy decoding, the same problems and settings for every model, on a MacBook Pro M5 Max (128 GB), 10 November 2026.

40 problems, 3,072-token cap, both models at 4-bit:

	Plain Qwen3.8-27B 4-bit	Qwen Concept 27B 4-bit
Thinking tokens, mean	1,746	1,202 (31% fewer)
Thinking tokens, median	1,477	673
Finished thinking within the cap	58%	83%
Correct final answer	55%	73%
Time for the run	19 min	12 min

The first 16 of those problems with a 2,048-token cap, mean thinking tokens: plain Qwen3.8-27B 4-bit 1,178; Qwen Concept 4-bit 876, 6-bit 801, 8-bit 909. Concept Country reports 27 to 54% fewer thinking tokens on its own benchmarks at Q8, so the effect carries over to every build here. The plain model is mlx-community/Qwen3.8-27B-4bit. Answers are scored by exact match of the boxed result; most of the accuracy difference comes from finishing within the cap more often.

"Same next token" in the table above compares each build with the MLX bf16 build over 12 prompts (thinking on and off), 96 teacher-forced tokens each.

Use

pip install -U mlx-lm        # text
mlx_lm.generate --model netprojects/Qwen-Concept-27B-MLX-8bit --prompt "How many primes are there below 100?" --max-tokens 4096

pip install -U mlx-vlm       # text and images
mlx_vlm.generate --model netprojects/Qwen-Concept-27B-MLX-8bit --image photo.jpg --prompt "Describe the scene." --max-tokens 1024

mlx_lm.server and mlx_vlm.server serve it with an OpenAI-compatible API. Tested with mlx 0.32.4, mlx-lm 0.32.2 and mlx-vlm 0.7.11. For thinking mode the Qwen3.8 card recommends temperature 1.0, top-p 0.95, top-k 20.

What was checked

The edit is intact. Compared tensor by tensor with plain Qwen3.8-27B, 842 of 851 language-model tensors are identical and exactly nine differ: mlp.down_proj of layers 32, 34, ..., 48, each by 0.4 to 0.6% in norm. This matches the EDIT_SPEC.json that ships with the model and is included here.
Source files. All 33 files of the upstream bf16 folder match its SHA256SUMS.
Images. The 4-bit, 6-bit and 8-bit builds each describe a test image correctly in mlx-vlm.

License

Apache-2.0, as the original model and Qwen3.8-27B. LICENSE is the Apache-2.0 text from the upstream repository and NOTICE describes the conversion and credits Concept Country and Alibaba Cloud.