Instructions to use mlx-community/Kimi-Linear-48B-A3B-Instruct-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Kimi-Linear-48B-A3B-Instruct-5bit 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/Kimi-Linear-48B-A3B-Instruct-5bit") 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/Kimi-Linear-48B-A3B-Instruct-5bit 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/Kimi-Linear-48B-A3B-Instruct-5bit"
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/Kimi-Linear-48B-A3B-Instruct-5bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/Kimi-Linear-48B-A3B-Instruct-5bit 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/Kimi-Linear-48B-A3B-Instruct-5bit"
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/Kimi-Linear-48B-A3B-Instruct-5bit
Run Hermes
hermes
- MLX LM
How to use mlx-community/Kimi-Linear-48B-A3B-Instruct-5bit 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/Kimi-Linear-48B-A3B-Instruct-5bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Kimi-Linear-48B-A3B-Instruct-5bit" # 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/Kimi-Linear-48B-A3B-Instruct-5bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
| "architectures": [ | |
| "KimiLinearForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_kimi.KimiLinearConfig", | |
| "AutoModel": "modeling_kimi.KimiLinearModel", | |
| "AutoModelForCausalLM": "modeling_kimi.KimiLinearForCausalLM" | |
| }, | |
| "bos_token_id": 163584, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 163586, | |
| "first_k_dense_replace": 1, | |
| "head_dim": 72, | |
| "hidden_act": "silu", | |
| "hidden_size": 2304, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 9216, | |
| "kv_lora_rank": 512, | |
| "linear_attn_config": { | |
| "full_attn_layers": [ | |
| 4, | |
| 8, | |
| 12, | |
| 16, | |
| 20, | |
| 24, | |
| 27 | |
| ], | |
| "head_dim": 128, | |
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| 2, | |
| 3, | |
| 5, | |
| 6, | |
| 7, | |
| 9, | |
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| 11, | |
| 13, | |
| 14, | |
| 15, | |
| 17, | |
| 18, | |
| 19, | |
| 21, | |
| 22, | |
| 23, | |
| 25, | |
| 26 | |
| ], | |
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| "mla_use_nope": true, | |
| "model_max_length": 1048576, | |
| "model_type": "kimi_linear", | |
| "moe_intermediate_size": 1024, | |
| "moe_layer_freq": 1, | |
| "moe_renormalize": true, | |
| "moe_router_activation_func": "sigmoid", | |
| "num_attention_heads": 32, | |
| "num_expert_group": 1, | |
| "num_experts": 256, | |
| "num_experts_per_token": 8, | |
| "num_hidden_layers": 27, | |
| "num_key_value_heads": 32, | |
| "num_nextn_predict_layers": 0, | |
| "num_shared_experts": 1, | |
| "pad_token_id": 163839, | |
| "q_lora_rank": null, | |
| "qk_nope_head_dim": 128, | |
| "qk_rope_head_dim": 64, | |
| "quantization": { | |
| "group_size": 64, | |
| "bits": 5, | |
| "mode": "affine", | |
| "model.layers.1.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| }, | |
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| "bits": 8 | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "group_size": 64, | |
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| "group_size": 64, | |
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| "group_size": 64, | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "model.layers.25.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
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| "model.layers.26.mlp.gate": { | |
| "group_size": 64, | |
| "bits": 8 | |
| } | |
| }, | |
| "quantization_config": { | |
| "group_size": 64, | |
| "bits": 5, | |
| "mode": "affine", | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "group_size": 64, | |
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| "group_size": 64, | |
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| "bits": 8 | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "bits": 8 | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "group_size": 64, | |
| "bits": 8 | |
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| "group_size": 64, | |
| "bits": 8 | |
| }, | |
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| "group_size": 64, | |
| "bits": 8 | |
| } | |
| }, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "routed_scaling_factor": 2.446, | |
| "tie_word_embeddings": false, | |
| "topk_group": 1, | |
| "transformers_version": "4.57.1", | |
| "use_cache": true, | |
| "use_grouped_topk": true, | |
| "v_head_dim": 128, | |
| "vocab_size": 163840 | |
| } |