Text Generation
MLX
Safetensors
English
mistral3
rotorquant
kv-cache-quantization
mistral
Mixture of Experts
sparse-moe
multimodal
quantized
4-bit precision
apple-silicon
256k-context
thinking
conversational
Instructions to use majentik/Mistral-Small-4-119B-RotorQuant-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use majentik/Mistral-Small-4-119B-RotorQuant-MLX-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("majentik/Mistral-Small-4-119B-RotorQuant-MLX-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
- Pi
How to use majentik/Mistral-Small-4-119B-RotorQuant-MLX-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 "majentik/Mistral-Small-4-119B-RotorQuant-MLX-4bit"
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": "majentik/Mistral-Small-4-119B-RotorQuant-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use majentik/Mistral-Small-4-119B-RotorQuant-MLX-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 "majentik/Mistral-Small-4-119B-RotorQuant-MLX-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 "majentik/Mistral-Small-4-119B-RotorQuant-MLX-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 LM
How to use majentik/Mistral-Small-4-119B-RotorQuant-MLX-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 "majentik/Mistral-Small-4-119B-RotorQuant-MLX-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "majentik/Mistral-Small-4-119B-RotorQuant-MLX-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "majentik/Mistral-Small-4-119B-RotorQuant-MLX-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use majentik/Mistral-Small-4-119B-RotorQuant-MLX-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 "majentik/Mistral-Small-4-119B-RotorQuant-MLX-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 majentik/Mistral-Small-4-119B-RotorQuant-MLX-4bit
Run Hermes
hermes
File size: 1,510 Bytes
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"dim": 4096,
"n_layers": 36,
"head_dim": 128,
"hidden_dim": 12288,
"n_heads": 32,
"n_kv_heads": 32,
"rope_theta": 10000.0,
"norm_eps": 1e-06,
"vocab_size": 131072,
"tied_embeddings": false,
"max_position_embeddings": 1048576,
"llama_4_scaling": {
"original_max_position_embeddings": 8192,
"beta": 0.1
},
"q_lora_rank": 1024,
"qk_rope_head_dim": 64,
"qk_nope_head_dim": 64,
"kv_lora_rank": 256,
"v_head_dim": 128,
"quantization": {
"qformat_weight": "fp8_e4m3",
"qscheme_act": "TENSOR"
},
"yarn": {
"original_max_position_embeddings": 8192,
"factor": 128,
"apply_scale": false,
"beta": 32,
"alpha": 1
},
"moe": {
"expert_parallel": 1,
"expert_model_parallel": 1,
"route_every_n": 1,
"first_k_dense_replace": 0,
"num_experts": 128,
"num_experts_per_tok": 4,
"num_expert_groups": 1,
"num_expert_groups_per_tok": 1,
"routed_scale": 1.0,
"expert_hidden_dim": 2048,
"num_shared_experts": 1
},
"vision_encoder": {
"image_token_id": 10,
"image_break_token_id": 12,
"image_end_token_id": 13,
"intermediate_size": 4096,
"num_hidden_layers": 24,
"num_attention_heads": 16,
"mm_projector_id": "patch_merge",
"spatial_merge_size": 2,
"hidden_size": 1024,
"num_channels": 3,
"image_size": 1540,
"max_image_size": 1540,
"patch_size": 14,
"rope_theta": 10000.0,
"add_pre_mm_projector_layer_norm": true,
"adapter_bias": false
}
} |