Instructions to use robbiemu/MobileLLM-R1-950M-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use robbiemu/MobileLLM-R1-950M-MLX 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("robbiemu/MobileLLM-R1-950M-MLX")

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)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use robbiemu/MobileLLM-R1-950M-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "robbiemu/MobileLLM-R1-950M-MLX"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "robbiemu/MobileLLM-R1-950M-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use robbiemu/MobileLLM-R1-950M-MLX with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "robbiemu/MobileLLM-R1-950M-MLX"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default robbiemu/MobileLLM-R1-950M-MLX

Run Hermes

hermes

MLX LM

How to use robbiemu/MobileLLM-R1-950M-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "robbiemu/MobileLLM-R1-950M-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "robbiemu/MobileLLM-R1-950M-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "robbiemu/MobileLLM-R1-950M-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

MobileLLM-R1-950M-MLX / quantization.log

robbiemu

add mlx and mlx-lm support

e39ff3a 9 months ago

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	uv run python custom_mlx_lm/custom_convert.py --hf-path . --mlx-path MobileLLM-R1-950M-mixed-4bit-mlx --dynamic-quant --target-bpw 4.5 --group-size 64 --report-ppl
	Loading model from ....
	Loading calibration data...
	Token indices sequence length is longer than the specified maximum sequence length for this model (110205 > 32768). Running this sequence through the model will result in indexing errors
	Calculating perplexity of original model...
	Original PPL: 50.262
	Starting advanced mixed-precision quantization...
	huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
	To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true \| false)
	Estimating sensitivities: 100%\|████████████████████████████████████\| 54/54 [02:03<00:00, 2.28s/it]
	Calculating perplexity of quantized model...
	Quantized PPL: 59.059

	✅ Model saved to MobileLLM-R1-950M-mixed-4bit-mlx

	uv run python custom_mlx_lm/quant_summary.py --model-path MobileLLM-R1-950M-mixed-4bit-mlx --show 8
	Method: mixed_precision_dynamic
	Group size: 64
	Total linear layers: 154
	4-bit layers: 153
	8-bit layers: 1

	Examples (8-bit):
	- layers.0.attention.o_proj

	Examples (4-bit):
	- layers.0.attention.k_proj
	- layers.0.attention.q_proj
	- layers.0.attention.v_proj
	- layers.0.feed_forward.down_proj
	- layers.0.feed_forward.gate_proj
	- layers.0.feed_forward.up_proj
	- layers.1.attention.k_proj
	- layers.1.attention.o_proj

	weights.npz contains quantized tensors: True