Instructions to use hyper-accel/tiny-random-kimi-linear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hyper-accel/tiny-random-kimi-linear with Transformers:

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

pipe = pipeline("text-generation", model="hyper-accel/tiny-random-kimi-linear", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("hyper-accel/tiny-random-kimi-linear", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hyper-accel/tiny-random-kimi-linear with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hyper-accel/tiny-random-kimi-linear

SGLang

How to use hyper-accel/tiny-random-kimi-linear 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 "hyper-accel/tiny-random-kimi-linear" \
    --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": "hyper-accel/tiny-random-kimi-linear",
		"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 "hyper-accel/tiny-random-kimi-linear" \
        --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": "hyper-accel/tiny-random-kimi-linear",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hyper-accel/tiny-random-kimi-linear with Docker Model Runner:
```
docker model run hf.co/hyper-accel/tiny-random-kimi-linear
```

tiny-random-kimi-linear / tokenizer_config.json

HyperAccel

Upload tokenizer from kimi_linear

c966327 verified about 2 months ago

raw

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2.75 kB

	{
	"added_tokens_decoder": {
	"163584": {
	"content": "[BOS]",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163585": {
	"content": "[EOS]",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163586": {
	"content": "<\|im_end\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163587": {
	"content": "<\|im_user\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163588": {
	"content": "<\|im_assistant\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163590": {
	"content": "<\|start_header_id\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163591": {
	"content": "<\|end_header_id\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163593": {
	"content": "[EOT]",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163594": {
	"content": "<\|im_system\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163601": {
	"content": "<\|im_middle\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163838": {
	"content": "[UNK]",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"163839": {
	"content": "[PAD]",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"auto_map": {
	"AutoTokenizer": [
	"tokenization_kimi.TikTokenTokenizer",
	null
	]
	},
	"backend": "custom",
	"bos_token": "[BOS]",
	"clean_up_tokenization_spaces": false,
	"eos_token": "[EOS]",
	"extra_special_tokens": [
	"<\|im_end\|>",
	"<\|im_user\|>",
	"<\|im_assistant\|>",
	"<\|start_header_id\|>",
	"<\|end_header_id\|>",
	"[EOT]",
	"<\|im_system\|>",
	"<\|im_middle\|>"
	],
	"is_local": false,
	"model_max_length": 1000000000000000019884624838656,
	"pad_token": "[PAD]",
	"tokenizer_class": "TikTokenTokenizer",
	"unk_token": "[UNK]"
	}