Instructions to use lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit 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("lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit")

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 lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit 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 "lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit"

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 lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "lmstudio-community/Llama-4-Scout-17B-16E-MLX-text-8bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Llama-4-Scout-17B-16E-MLX-text-8bit / config.json

neilmehta24

Update config.json

5e44dbb verified over 1 year ago

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	{
	"architectures": [
	"Llama4ForConditionalGeneration"
	],
	"boi_token_index": 200080,
	"eoi_token_index": 200081,
	"image_token_index": 200092,
	"model_type": "llama4",
	"quantization": {
	"group_size": 64,
	"bits": 8
	},
	"quantization_config": {
	"group_size": 64,
	"bits": 8
	},
	"text_config": {
	"_attn_implementation_autoset": true,
	"attention_bias": false,
	"attention_chunk_size": 8192,
	"attention_dropout": 0.0,
	"bos_token_id": 200000,
	"eos_token_id": [
	200001,
	200007,
	200008
	],
	"for_llm_compressor": false,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 5120,
	"initializer_range": 0.02,
	"interleave_moe_layer_step": 1,
	"intermediate_size": 8192,
	"intermediate_size_mlp": 16384,
	"max_position_embeddings": 10485760,
	"model_type": "llama4_text",
	"no_rope_layers": [],
	"num_attention_heads": 40,
	"num_experts_per_tok": 1,
	"num_hidden_layers": 48,
	"num_key_value_heads": 8,
	"num_local_experts": 16,
	"output_router_logits": false,
	"pad_token_id": 200018,
	"rms_norm_eps": 1e-05,
	"rope_scaling": {
	"factor": 8.0,
	"high_freq_factor": 4.0,
	"low_freq_factor": 1.0,
	"original_max_position_embeddings": 8192,
	"rope_type": "llama3"
	},
	"rope_theta": 500000.0,
	"router_aux_loss_coef": 0.001,
	"router_jitter_noise": 0.0,
	"torch_dtype": "bfloat16",
	"use_cache": true,
	"use_qk_norm": true,
	"vocab_size": 202048
	},
	"torch_dtype": "bfloat16",
	"transformers_version": "4.51.0.dev0"
	}