Instructions to use mlx-community/Mistral-Large-4-1T-A52B-Text-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Mistral-Large-4-1T-A52B-Text-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("mlx-community/Mistral-Large-4-1T-A52B-Text-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)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use mlx-community/Mistral-Large-4-1T-A52B-Text-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Mistral-Large-4-1T-A52B-Text-4bit"

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": "mlx-community/Mistral-Large-4-1T-A52B-Text-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use mlx-community/Mistral-Large-4-1T-A52B-Text-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/Mistral-Large-4-1T-A52B-Text-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Mistral-Large-4-1T-A52B-Text-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/Mistral-Large-4-1T-A52B-Text-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use mlx-community/Mistral-Large-4-1T-A52B-Text-4bit 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 "mlx-community/Mistral-Large-4-1T-A52B-Text-4bit"

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 mlx-community/Mistral-Large-4-1T-A52B-Text-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/Mistral-Large-4-1T-A52B-Text-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Mistral-Large-4-1T-A52B-Text-4bit"

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 "mlx-community/Mistral-Large-4-1T-A52B-Text-4bit" \
  --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"

mlx-community/Mistral-Large-4-1T-A52B-Text-4bit

This model mlx-community/Mistral-Large-4-1T-A52B-Text-4bit was converted to MLX format from mistralai/Mistral-Large-4-1T-A52B using mlx-lm with ml-explore/mlx-lm#2018. Refer to the original model card for more details on the model.

This is a text-only conversion. The vision encoder and the DSpark draft head are not included.

The FP8 weights were dequantized and then quantized to 4-bit affine with group size 64 (4.5 bits per weight). The router and two indexer projections stay in bf16, as in the original checkpoint. It does not fit in the memory of one 512GB Mac. Peak memory was about 300 GB on each of two Macs in a short generation test with pipeline parallelism.

Tested with mlx-lm 0.32.3.dev13 and MLX 0.32.4.

Use with mlx

pip install -U mlx-lm

Run it with pipeline parallelism over two Macs, for example with the sharded_generate.py example of mlx-lm:

mlx.launch --backend ring --hostfile hosts.json \
    sharded_generate.py --model mlx-community/Mistral-Large-4-1T-A52B-Text-4bit --pipeline --prompt "hello"

See the MLX distributed guide for the host file and the network setup.

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Safetensors

Model size

1T params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for mlx-community/Mistral-Large-4-1T-A52B-Text-4bit

Base model

mistralai/Mistral-Large-4-1T-A52B

Quantized

(10)

this model