Instructions to use DreamFoundries/supergemma4-e4b-abliterated-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use DreamFoundries/supergemma4-e4b-abliterated-6bit 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("DreamFoundries/supergemma4-e4b-abliterated-6bit") 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
- MLX LM
How to use DreamFoundries/supergemma4-e4b-abliterated-6bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "DreamFoundries/supergemma4-e4b-abliterated-6bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "DreamFoundries/supergemma4-e4b-abliterated-6bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DreamFoundries/supergemma4-e4b-abliterated-6bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
supergemma4-e4b-abliterated MLX 6-bit
This repository contains an MLX-LM conversion of Jiunsong/supergemma4-e4b-abliterated.
Conversion Details
- Original model:
Jiunsong/supergemma4-e4b-abliterated - Model family: SuperGemma4
- Source model type:
gemma4 - Model size: 7,518,068,992 parameters
- Quantization: MLX-LM affine quantization
- Bits: 6-bit
- Group size: 64
- Local MLX folder size at upload time: 5.71 GiB
- Local safetensors weight size at upload time: 5.68 GiB
This Gemma conversion follows the MLX-LM Gemma 4 shared-KV topology and uses non-strict checkpoint loading so extra HF tensors outside that topology are discarded during conversion.
For mlx-swift compatibility, per_layer_model_projection was left unquantized while the rest of the eligible linear layers were quantized.
For oMLX / mlx-swift chat-template compatibility, the published MLX package uses a simplified Gemma 4 chat template based on the <|turn>role\n...<turn|> format. The original upstream chat_template.jinja is preserved as chat_template.original.jinja.
Abliteration credit: the abliterated source model was published by Jiunsong. This repository only provides the MLX conversion and quantization.
Usage
mlx_lm.generate --model DreamFoundries/supergemma4-e4b-abliterated-6bit --prompt "Hello" --max-tokens 64
Benchmarks
No comparative benchmarks have been run yet. The repository does not currently provide quality, speed, memory, or benchmark comparisons against the original weights or other quantizations.
License
This is a converted/quantized derivative of the original model. Please refer to the original model repository for the upstream license and usage terms: https://huggingface.co/Jiunsong/supergemma4-e4b-abliterated
- Downloads last month
- 118
6-bit
Model tree for DreamFoundries/supergemma4-e4b-abliterated-6bit
Base model
google/gemma-4-E4B