Rao On-device Base Set
Collection
The base on-device models that make Ambient, Veil, Craft, Ambient Intelligence work. โข 7 items โข Updated
How to use rao-studios/Mistral-Nemo-Instruct-2407-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("rao-studios/Mistral-Nemo-Instruct-2407-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)How to use rao-studios/Mistral-Nemo-Instruct-2407-mlx-4bit with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "rao-studios/Mistral-Nemo-Instruct-2407-mlx-4bit"
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "rao-studios/Mistral-Nemo-Instruct-2407-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": "rao-studios/Mistral-Nemo-Instruct-2407-mlx-4bit",
"messages": [
{"role": "user", "content": "Hello"}
]
}'Rao Studios' byte-identical mirror of
mlx-community/Mistral-Nemo-Instruct-2407-4bit
at commit 647ca0751669b21a364c86ccc5df54c4d7e4e91c, which mlx-community converted from
mistralai/Mistral-Nemo-Instruct-2407
with mlx-lm 0.16.0. Every weight and tokenizer file is unchanged; only this card and LICENSE.txt are added.
MistralForCausalLM, 12B parameters, 40 layers, 128k-vocabulary tokenizer (131072)LICENSE.txt)Mirrored so the models Rao's on-device stack depends on (Ambient, Craft, Veil, via AII) are pinned to a repository Rao controls.
4-bit