Instructions to use mlx-community/RakutenAI-2.0-8x7B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/RakutenAI-2.0-8x7B-MLX-8bit 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("mlx-community/RakutenAI-2.0-8x7B-MLX-8bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/RakutenAI-2.0-8x7B-MLX-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/RakutenAI-2.0-8x7B-MLX-8bit" --prompt "Once upon a time"
This model was converted to MLX format from Rakuten/RakutenAI-2.0-8x7B using mlx-lm version 0.31.0.
Refer to the original model card for more details on the model.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/RakutenAI-2.0-8x7B-MLX-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
Inference: M3 Ultra
==========
world - 简书
hello world
hello world
hello world
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==========
Prompt: 2 tokens, 5.448 tokens-per-sec
Generation: 256 tokens, 42.701 tokens-per-sec
Peak memory: 49.838 GB
- Downloads last month
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Model size
47B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
Log In to add your hardware
8-bit
Model tree for mlx-community/RakutenAI-2.0-8x7B-MLX-8bit
Base model
mistralai/Mistral-7B-v0.1 Finetuned
Rakuten/RakutenAI-7B Finetuned
Rakuten/RakutenAI-2.0-8x7B