mlx-llm 1.0.9

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 🧠

Currently, out-of-the-box supported models are:

Family	Models
LLaMA 2	llama_2_7b_chat_hf, llama_2_7b_hf
LLaMA 3	deepseek_r1_distill_llama_8b, llama_3_8b, llama_3_8b_instruct, hermes_2_pro_llama_3_8b, llama_3_2_1b_instruct, llama_3_2_3b_instruct
Phi3	phi_3_mini_4k_instruct, phi_3_mini_128k_instruct, phi_3.5_mini_instruct
Mistral	mistral_7b_instruct_v0.2, openhermes_2.5_mistral_7b, starling_lm_7b_beta
TinyLLaMA	tiny_llama_1.1B_chat_v1.0
Gemma	gemma_1.1_2b_it, gemma_1.1_7b_it, gemma_2_2b_it, gemma_2_9b_it
OpenELM	openelm_270M_instruct, openelm_450M_instruct, openelm_1.1B_instruct, openelm_3B_instruct
SmolLM2	smollm2_1.7B_instruct, smollm2_360M_instruct, smollm2_135M_instruct

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

from mlx_llm.model import create_model

# loading weights from HuggingFace
model = create_model("deepseek_r1_distill_llama_8b")

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
• 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

# an example of loading new weights from HuggingFace
model = create_model(
    model_name="openelm_1.1B_instruct", # it's the base model
    weights="hf://apple/OpenELM-1.1B", # new weights from HuggingFace
)

# an example of loading new weights from HuggingFace with custom model configs
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
    model_config={
        "rope_theta": 207112184.0
    }
)

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)

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

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 him, 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()

[!WARNING] OpenELM chat-mode is broken. I am working on fixing it.

📧 Contact

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