Text Generation
Transformers
Safetensors
MLX
English
gemma3
image-text-to-text
uncensored
direct-answer
information-retrieval
general-knowledge
unfiltered
amoral-ai
mlx-my-repo
conversational
text-generation-inference
4-bit precision
Instructions to use Disya/GrayLine-Gemma3-4B-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Disya/GrayLine-Gemma3-4B-mlx-4Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Disya/GrayLine-Gemma3-4B-mlx-4Bit") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Disya/GrayLine-Gemma3-4B-mlx-4Bit") model = AutoModelForImageTextToText.from_pretrained("Disya/GrayLine-Gemma3-4B-mlx-4Bit") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - MLX
How to use Disya/GrayLine-Gemma3-4B-mlx-4Bit 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("Disya/GrayLine-Gemma3-4B-mlx-4Bit") 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
- LM Studio
- vLLM
How to use Disya/GrayLine-Gemma3-4B-mlx-4Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Disya/GrayLine-Gemma3-4B-mlx-4Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Disya/GrayLine-Gemma3-4B-mlx-4Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Disya/GrayLine-Gemma3-4B-mlx-4Bit
- SGLang
How to use Disya/GrayLine-Gemma3-4B-mlx-4Bit with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Disya/GrayLine-Gemma3-4B-mlx-4Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Disya/GrayLine-Gemma3-4B-mlx-4Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Disya/GrayLine-Gemma3-4B-mlx-4Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Disya/GrayLine-Gemma3-4B-mlx-4Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - MLX LM
How to use Disya/GrayLine-Gemma3-4B-mlx-4Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Disya/GrayLine-Gemma3-4B-mlx-4Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Disya/GrayLine-Gemma3-4B-mlx-4Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Disya/GrayLine-Gemma3-4B-mlx-4Bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use Disya/GrayLine-Gemma3-4B-mlx-4Bit with Docker Model Runner:
docker model run hf.co/Disya/GrayLine-Gemma3-4B-mlx-4Bit
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