Instructions to use ressl/gemma-4-31B-it-uncensored-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ressl/gemma-4-31B-it-uncensored-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("ressl/gemma-4-31B-it-uncensored-MLX-4bit")
config = load_config("ressl/gemma-4-31B-it-uncensored-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 ressl/gemma-4-31B-it-uncensored-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 "ressl/gemma-4-31B-it-uncensored-MLX-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": "ressl/gemma-4-31B-it-uncensored-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use ressl/gemma-4-31B-it-uncensored-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 "ressl/gemma-4-31B-it-uncensored-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 ressl/gemma-4-31B-it-uncensored-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ressl/gemma-4-31B-it-uncensored-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 "ressl/gemma-4-31B-it-uncensored-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 "ressl/gemma-4-31B-it-uncensored-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"

ressl/gemma-4-31B-it-uncensored-MLX-4bit

MLX conversion of ressl/gemma-4-31B-it-uncensored for Apple silicon. This release uses 4-bit affine quantization with group size 64 and preserves the multimodal vision path in BF16.

This is an uncensored, abliterated research model. It can produce inaccurate, unsafe, illegal, biased, or offensive content. Outputs are not advice. Evaluate the model for your use case, keep human oversight, and comply with applicable law and the Apache License 2.0.

Security research, red teaming, robustness evaluation, and defensive model analysis are the intended uses. Do not use this release to harm people or systems.

Release family

Format	Repository
Transformers BF16 source	ressl/gemma-4-31B-it-uncensored
NVIDIA NVFP4	ressl/gemma-4-31B-it-uncensored-NVFP4
GGUF quantization ladder	ressl/gemma-4-31B-it-uncensored-GGUF
MLX 4-bit	ressl/gemma-4-31B-it-uncensored-MLX-4bit
MLX 5-bit	ressl/gemma-4-31B-it-uncensored-MLX-5bit
MLX 6-bit	ressl/gemma-4-31B-it-uncensored-MLX-6bit
MLX 8-bit	ressl/gemma-4-31B-it-uncensored-MLX-8bit
MLX BF16	ressl/gemma-4-31B-it-uncensored-MLX-bf16

Verified release facts

Property	Measured value
Source revision	`64c863e92fc131e5f4b0fe3631a0791fe2c19152`
Weight format	4-bit affine quantization with group size 64
Weight size	17.16 GiB
Weight shards	4
Files in artifact	15
Vision path	BF16, not quantized
Prompt processing	462.454 tokens/s
Generation	27.967 tokens/s
Peak memory	19.282 GB
Conversion and inference stack	`mlx-vlm==0.6.4`

The performance values above are measurements from the release smoke run. They are not estimates or cross-device promises.

Refusal evaluation

The release gate completed all 686 prompts with zero execution errors. A naive keyword heuristic detected broad refusal-like language, while the stricter hard-refusal detector found 0/686 responses that refused without providing substantive help.

Dataset	Successful	Naive refusals
JailbreakBench	100/100	79
tulu-harmbench	320/320	131
NousResearch	166/166	115
mlabonne	100/100	83
Total	686/686	408

No hard refusals were detected, so no refusal review was triggered.

Keyword metrics are imperfect and do not prove capability, factuality, or safety. The exact deterministic evaluator and release criteria live in the source repository.

Run on Apple silicon

Install the exact tested version:

python -m pip install mlx-vlm==0.6.4

Text generation:

mlx_vlm.generate --model ressl/gemma-4-31B-it-uncensored-MLX-4bit --prompt "Explain why Alpine flowers survive harsh winters."

Image and text generation:

mlx_vlm.generate --model ressl/gemma-4-31B-it-uncensored-MLX-4bit --image path/to/image.jpg --prompt "Describe this image in detail."

Thinking mode:

mlx_vlm.generate --model ressl/gemma-4-31B-it-uncensored-MLX-4bit --prompt "Solve 37 * 48 step by step." --enable-thinking

OpenAI-compatible local server:

mlx_vlm.server --model ressl/gemma-4-31B-it-uncensored-MLX-4bit --host 127.0.0.1 --port 8080

Quality and limitations

The language model weights use 4-bit affine quantization with group size 64. Quantization can reduce quality compared with BF16.
The vision tower and vision embedding path remain BF16 in every MLX release.
The smoke suite verifies deterministic arithmetic, capitals, German, multi-turn memory, thinking mode, image understanding, basic output health, and measured runtime statistics.
This checkpoint inherits the source model's limitations and may hallucinate or follow malicious instructions.
No benchmark result should be generalized beyond the exact prompts, software, and hardware used for the measured run.

Provenance

The checkpoint was converted from commit 64c863e92fc131e5f4b0fe3631a0791fe2c19152. Quantized releases use affine MLX quantization with group size 64 for language weights. Modules whose path contains vision_tower or embed_vision are excluded from quantization. The BF16 release performs no weight quantization.

The original Gemma architecture and weights are provided by Google under the Apache License 2.0. This MLX conversion and the uncensored source release are maintained by Robert Ressl (Hugging Face, Website, LinkedIn, Patreon).

If this work is useful, you can support continued independent model research on Patreon.

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Safetensors

Model size

31B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for ressl/gemma-4-31B-it-uncensored-MLX-4bit

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