Instructions to use moonshotai/Kimi-Linear-48B-A3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moonshotai/Kimi-Linear-48B-A3B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="moonshotai/Kimi-Linear-48B-A3B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("moonshotai/Kimi-Linear-48B-A3B-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("moonshotai/Kimi-Linear-48B-A3B-Instruct", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moonshotai/Kimi-Linear-48B-A3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moonshotai/Kimi-Linear-48B-A3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moonshotai/Kimi-Linear-48B-A3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/moonshotai/Kimi-Linear-48B-A3B-Instruct

SGLang

How to use moonshotai/Kimi-Linear-48B-A3B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "moonshotai/Kimi-Linear-48B-A3B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moonshotai/Kimi-Linear-48B-A3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "moonshotai/Kimi-Linear-48B-A3B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moonshotai/Kimi-Linear-48B-A3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use moonshotai/Kimi-Linear-48B-A3B-Instruct with Docker Model Runner:
```
docker model run hf.co/moonshotai/Kimi-Linear-48B-A3B-Instruct
```

Kimi-Linear-48B-A3B-Instruct / config.json

zhiyuan8

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{
  "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,
    "kda_layers": [
      1,
      2,
      3,
      5,
      6,
      7,
      9,
      10,
      11,
      13,
      14,
      15,
      17,
      18,
      19,
      21,
      22,
      23,
      25,
      26
    ],
    "num_heads": 32,
    "short_conv_kernel_size": 4
  },
  "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,
  "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
}

	{
	"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,
	"kda_layers": [
	1,
	2,
	3,
	5,
	6,
	7,
	9,
	10,
	11,
	13,
	14,
	15,
	17,
	18,
	19,
	21,
	22,
	23,
	25,
	26
	],
	"num_heads": 32,
	"short_conv_kernel_size": 4
	},
	"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,
	"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
	}