Text Generation
MLX
Safetensors
English
glm4_moe
prime-rl
verifiers
prime-intellect
reinforcement-learning
reasoning
agentic
mixture-of-experts
conversational
custom_code
Instructions to use mlx-community/INTELLECT-3-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/INTELLECT-3-bf16 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/INTELLECT-3-bf16") 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 mlx-community/INTELLECT-3-bf16 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/INTELLECT-3-bf16"
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": "mlx-community/INTELLECT-3-bf16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/INTELLECT-3-bf16 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/INTELLECT-3-bf16"
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/INTELLECT-3-bf16
Run Hermes
hermes
- MLX LM
How to use mlx-community/INTELLECT-3-bf16 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/INTELLECT-3-bf16"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/INTELLECT-3-bf16" # 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/INTELLECT-3-bf16", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 1,318 Bytes
ab898df | 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 | {
"architectures": [
"Glm4MoeForCausalLM"
],
"attention_bias": true,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_glm4_moe.Glm4MoeConfig",
"AutoModel": "modeling_glm4_moe.Glm4MoeModel",
"AutoModelForCausalLM": "modeling_glm4_moe.Glm4MoeForCausalLM"
},
"dtype": "bfloat16",
"eos_token_id": [
151334,
151329
],
"first_k_dense_replace": 1,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 10944,
"max_position_embeddings": 131072,
"model_type": "glm4_moe",
"moe_intermediate_size": 1408,
"n_group": 1,
"n_routed_experts": 128,
"n_shared_experts": 1,
"norm_topk_prob": true,
"num_attention_heads": 96,
"num_experts_per_tok": 8,
"num_hidden_layers": 46,
"num_key_value_heads": 8,
"num_nextn_predict_layers": 1,
"pad_token_id": 151329,
"partial_rotary_factor": 0.5,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 1000000,
"routed_scaling_factor": 1.0,
"tie_word_embeddings": false,
"topk_group": 1,
"transformers_version": "4.56.1",
"use_cache": false,
"use_grouped_mm": true,
"use_qk_norm": false,
"vocab_size": 151552
} |