Instructions to use isetnefret/Rose-20B-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use isetnefret/Rose-20B-mlx-6Bit 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("isetnefret/Rose-20B-mlx-6Bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use isetnefret/Rose-20B-mlx-6Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "isetnefret/Rose-20B-mlx-6Bit" --prompt "Once upon a time"
isetnefret/Rose-20B-mlx-6Bit
The Model isetnefret/Rose-20B-mlx-6Bit was converted to MLX format from tavtav/Rose-20B using mlx-lm version 0.22.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("isetnefret/Rose-20B-mlx-6Bit")
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
4B params
Tensor type
F16
·
U32 ·
Hardware compatibility
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6-bit
Model tree for isetnefret/Rose-20B-mlx-6Bit
Base model
tavtav/Rose-20B