mlx-llm 1.0.1

Large Language Models (LLMs) applications and tools running on Apple Silicon in real-time with Apple MLX

mlx-llm

Large Language Models (LLMs) applications and tools running on Apple Silicon in real-time with Apple MLX.

Go to the entire Youtube Video.

How to install 🔨

pip install mlx-llm

Models 🧠

To create a model with pre-trained weights from HuggingFace:

from mlx_llm.model import create_model

# loading weights from HuggingFace
model = create_model("llama_3_8b_instruct")

You can also load a new version of pre-trained weights for a specific model directly from HuggingFace:

• set weights by adding hf:// before the HuggingFace repository
• load the proper weights converter function
• if necessary, specify custom model configs (rope_theta, rope_traditional, vocab_size, norm_eps)

Here's an example of how to to it:

from mlx_llm.model import create_model
from mlx_llm.model.converter import llama_to_mlxllm

model = create_model(
    model_name="llama_3_8b_instruct", # it's the base model
    weights="hf://gradientai/Llama-3-8B-Instruct-262k", # new weights from HuggingFace
    converter=llama_to_mlxllm, # it's the weights converter function for the base model
    model_config={
        "rope_theta": 207112184.0
    }
)

To list all available models:

from mlx_llm.model import list_models

print(list_models())

Quantization 📉

To quantize a model and save its weights just use:

from mlx_llm.model import create_model, quantize, get_weights
from mlx_llm.utils.weights import save_weights

# create the model from original weights
model = create_model("llama_3_8b_instruct")
# quantize the model
model = quantize(model, group_size=64, bits=4)
# getting weights dict (similar to state_dict in PyTorch)
weights = get_weights(model)
# save the model
save_weights(weights, "llama_3_8b_instruct-4bit.safetensors")

Model Embeddings ✴️

Models in mlx-llm are able to extract embeddings from a given text.

import mlx.core as mx
from mlx_llm.model import create_model, create_tokenizer

model = create_model("llama_3_8b_instruct")
tokenizer = create_tokenizer('llama_3_8b_instruct')
text = ["I like to play basketball", "I like to play tennis"]
tokens = tokenizer(text)
x = mx.array(tokens["input_ids"])
embeds, _ = model.embed(x, norm=True)

Applications 📁

With mlx-llm you can run a variety of applications, such as:

• Chat with an LLM running on Apple Silicon on a Command Line interface
• Fine-Tuning a model with LoRA or QLoRA
• Retrieval Augmented Generation (RAG) for Question Answering

Chat with LLM 📱

mlx-llm comes with tools to easily run your LLM chat on Apple Silicon.

To chat with an LLM provide:

• a system prompt --> to set the overall tone of the LLM
• optional previous interactions to set the mood of the conversation

from mlx_llm.chat import ChatSetup, LLMChat
from mlx_llm.model import create_model, create_tokenizer
from mlx_llm.prompt import create_prompt

model_name = "tiny_llama_1.1B_chat_v1.0"

chat = LLMChat(
    model_name=model_name,
    chat_setup=ChatSetup(
        system="You are Michael Scott from The Office. Your goal is to answer like hime, so be funny and inappropriate, but be brief.",
        history=[
            {"question": "What is your name?", "answer": "Michael Scott"},
            {"question": "What is your favorite episode of The Office?", "answer": "The Dinner Party"},
        ],
    ),
    quantized=False, # if you want it faster use the quantization params (e.g., group_size=64, bits=4)
)

chat.start()

Fine-Tuning with LoRA or QLoRA 🚀

raise NotImplementedError

Retrieval Augmented Generation (RAG) 📚

raise NotImplementedError

ToDos

[ ] LoRA and QLoRA

[ ] RAG

📧 Contact

If you have any questions, please email [e-mail]