Text Generation
NeMo
Safetensors
MLX
English
mistral
sft
unsloth
style-tune
roleplay
creative
12b
mlx-my-repo
conversational
5-bit
Instructions to use ewald1976/Silver-Siren-ST-12B-mlx-5Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use ewald1976/Silver-Siren-ST-12B-mlx-5Bit with NeMo:
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- MLX
How to use ewald1976/Silver-Siren-ST-12B-mlx-5Bit 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("ewald1976/Silver-Siren-ST-12B-mlx-5Bit") 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
- Unsloth Studio
How to use ewald1976/Silver-Siren-ST-12B-mlx-5Bit with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ewald1976/Silver-Siren-ST-12B-mlx-5Bit to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ewald1976/Silver-Siren-ST-12B-mlx-5Bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ewald1976/Silver-Siren-ST-12B-mlx-5Bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ewald1976/Silver-Siren-ST-12B-mlx-5Bit", max_seq_length=2048, ) - MLX LM
How to use ewald1976/Silver-Siren-ST-12B-mlx-5Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ewald1976/Silver-Siren-ST-12B-mlx-5Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ewald1976/Silver-Siren-ST-12B-mlx-5Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ewald1976/Silver-Siren-ST-12B-mlx-5Bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
ewald1976/Silver-Siren-ST-12B-mlx-5Bit
The Model ewald1976/Silver-Siren-ST-12B-mlx-5Bit was converted to MLX format from ewald1976/Silver-Siren-ST-12B using mlx-lm version 0.31.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("ewald1976/Silver-Siren-ST-12B-mlx-5Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
12B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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5-bit
Model tree for ewald1976/Silver-Siren-ST-12B-mlx-5Bit
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Vortex5/Silver-Siren-12B Finetuned
ewald1976/Silver-Siren-ST-12B