Instructions to use daslab-testing/Apertus-8B-Instruct-MLX-INT6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use daslab-testing/Apertus-8B-Instruct-MLX-INT6 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("daslab-testing/Apertus-8B-Instruct-MLX-INT6") 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
- LM Studio
- Pi new
How to use daslab-testing/Apertus-8B-Instruct-MLX-INT6 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "daslab-testing/Apertus-8B-Instruct-MLX-INT6"
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": "daslab-testing/Apertus-8B-Instruct-MLX-INT6" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use daslab-testing/Apertus-8B-Instruct-MLX-INT6 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 "daslab-testing/Apertus-8B-Instruct-MLX-INT6"
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 daslab-testing/Apertus-8B-Instruct-MLX-INT6
Run Hermes
hermes
- MLX LM
How to use daslab-testing/Apertus-8B-Instruct-MLX-INT6 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "daslab-testing/Apertus-8B-Instruct-MLX-INT6"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "daslab-testing/Apertus-8B-Instruct-MLX-INT6" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "daslab-testing/Apertus-8B-Instruct-MLX-INT6", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 1,617 Bytes
46db0e1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | {
"architectures": [
"ApertusForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "float32",
"eos_token_id": [
2,
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],
"hidden_act": "xielu",
"hidden_dropout": 0.0,
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 21504,
"max_position_embeddings": 65536,
"mlp_bias": false,
"model_type": "apertus",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 3,
"post_norm": false,
"qk_norm": true,
"quantization": {
"group_size": 64,
"bits": 6,
"mode": "affine",
"model.embed_tokens": {
"bits": 6,
"group_size": 64
},
"lm_head": {
"bits": 6,
"group_size": 64
}
},
"quantization_config": {
"group_size": 64,
"bits": 6,
"mode": "affine",
"model.embed_tokens": {
"bits": 6,
"group_size": 64
},
"lm_head": {
"bits": 6,
"group_size": 64
}
},
"rms_norm_eps": 1e-05,
"rope_parameters": {
"factor": 8.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_theta": 12000000,
"rope_type": "llama3",
"type": "llama3"
},
"rope_theta": 12000000,
"tie_word_embeddings": false,
"transformers_version": "5.8.1",
"use_cache": false,
"vocab_size": 131072
} |