Text Generation
Transformers
Safetensors
MLX
mistral
mergekit
Merge
roleplay
mlx-my-repo
text-generation-inference
4-bit precision
Instructions to use maddyboy/Starlit-Shadow-12B-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maddyboy/Starlit-Shadow-12B-mlx-4Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="maddyboy/Starlit-Shadow-12B-mlx-4Bit")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("maddyboy/Starlit-Shadow-12B-mlx-4Bit") model = AutoModelForCausalLM.from_pretrained("maddyboy/Starlit-Shadow-12B-mlx-4Bit", device_map="auto") - MLX
How to use maddyboy/Starlit-Shadow-12B-mlx-4Bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("maddyboy/Starlit-Shadow-12B-mlx-4Bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use maddyboy/Starlit-Shadow-12B-mlx-4Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "maddyboy/Starlit-Shadow-12B-mlx-4Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maddyboy/Starlit-Shadow-12B-mlx-4Bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/maddyboy/Starlit-Shadow-12B-mlx-4Bit
- SGLang
How to use maddyboy/Starlit-Shadow-12B-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 "maddyboy/Starlit-Shadow-12B-mlx-4Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maddyboy/Starlit-Shadow-12B-mlx-4Bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "maddyboy/Starlit-Shadow-12B-mlx-4Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maddyboy/Starlit-Shadow-12B-mlx-4Bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use maddyboy/Starlit-Shadow-12B-mlx-4Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "maddyboy/Starlit-Shadow-12B-mlx-4Bit" --prompt "Once upon a time"
- Docker Model Runner
How to use maddyboy/Starlit-Shadow-12B-mlx-4Bit with Docker Model Runner:
docker model run hf.co/maddyboy/Starlit-Shadow-12B-mlx-4Bit
- Atomic Chat
Download tokenizer.json from maddyboy/Starlit-Shadow-12B-mlx-4Bit: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/maddyboy/Starlit-Shadow-12B-mlx-4Bit/resolve/main/tokenizer.json
- Command line
-
hf download hf://maddyboy/Starlit-Shadow-12B-mlx-4Bit/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/maddyboy/Starlit-Shadow-12B-mlx-4Bit/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 8d2c2f0daa05743d54717aff085f129fa44dfcafb6aeeeca4a819ef91df7c0db
- Size of remote file:
- 17.1 MB
- SHA256:
- 33cb74e9ca1c0323b1be5f7e367b96bae218c22d0c4ed5b0edeec39f3a8755c3
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