Instructions to use MK4-Research/MK4-Ginko-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MK4-Research/MK4-Ginko-v1.5 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("MK4-Research/MK4-Ginko-v1.5")

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 MK4-Research/MK4-Ginko-v1.5 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MK4-Research/MK4-Ginko-v1.5"

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": "MK4-Research/MK4-Ginko-v1.5"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use MK4-Research/MK4-Ginko-v1.5 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "MK4-Research/MK4-Ginko-v1.5"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "MK4-Research/MK4-Ginko-v1.5"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "MK4-Research/MK4-Ginko-v1.5",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use MK4-Research/MK4-Ginko-v1.5 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 "MK4-Research/MK4-Ginko-v1.5"

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 MK4-Research/MK4-Ginko-v1.5

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MK4-Research/MK4-Ginko-v1.5 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MK4-Research/MK4-Ginko-v1.5"

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 "MK4-Research/MK4-Ginko-v1.5" \
  --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"

MK4-Ginko v1.5 repair, step 400

This is the repaired v1.5 step-400 checkpoint merged into a standalone MLX model. It is released for reproducibility and further testing. The model was merged in higher precision and quantized to 6-bit so the LoRA updates survive quantization; no adapter is needed at inference.

Training

The base is mlx-community/Qwen3.8-27B-4bit. The adapter was trained for 400 steps on the v1_5_repair security-review corpus: 576 training presentations (288 safe, 288 vulnerable) and 23 validation cases. The target format has short FLOW, GUARD, and final VERDICT lines. Training used assistant-only loss (mask_prompt: true), 32 LoRA layers, rank 32, scale 20, dropout 0.05, learning rate 1e-5, batch size 1 with two-step gradient accumulation, and sequence length 512.

The corpus passed the project's code-overlap check against held-out GuardBench and FBE items. It teaches a compact response format. It does not implement continuous memory compaction or persistent state between requests.

Evaluation and limitations

The saved step-400 adapter has a partial GuardBench run of 113/360 prompts: 1/38 guarded-safe prompts correct, 75/75 vulnerable prompts correct, and no missing verdicts. Balanced accuracy on this nonrepresentative partial slice is 51.3%. A complete 360-prompt score has not been established for this checkpoint or the merged 6-bit artifact. The partial result shows a severe false-positive problem: the model frequently calls protected code vulnerable. Do not treat its findings as verified vulnerabilities.

This card deliberately does not claim a 74% balanced GuardBench score; no saved v1.5 step-400 benchmark record supports that number. The result near 75% in local records belongs to a different, v2-derived model.

Use

python3 -m mlx_lm generate --model MK4-Research/MK4-Ginko-v1.5 \
  --prompt 'Review this code for security problems: <your code>'

Provide a focused code path. Check both its guard analysis and final verdict against the actual code. This model is an experimental research checkpoint, not a substitute for manual security review.

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Safetensors

Model size

27B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for MK4-Research/MK4-Ginko-v1.5

Base model

Qwen/Qwen3.8-27B

Quantized

mlx-community/Qwen3.8-27B-4bit

Finetuned

(2)

this model