Instructions to use kakaocorp/kanana-2-30b-a3b-base-2601 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kakaocorp/kanana-2-30b-a3b-base-2601 with Transformers:

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

pipe = pipeline("text-generation", model="kakaocorp/kanana-2-30b-a3b-base-2601")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kakaocorp/kanana-2-30b-a3b-base-2601")
model = AutoModelForCausalLM.from_pretrained("kakaocorp/kanana-2-30b-a3b-base-2601")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kakaocorp/kanana-2-30b-a3b-base-2601 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kakaocorp/kanana-2-30b-a3b-base-2601"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kakaocorp/kanana-2-30b-a3b-base-2601",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kakaocorp/kanana-2-30b-a3b-base-2601

SGLang

How to use kakaocorp/kanana-2-30b-a3b-base-2601 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 "kakaocorp/kanana-2-30b-a3b-base-2601" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kakaocorp/kanana-2-30b-a3b-base-2601",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "kakaocorp/kanana-2-30b-a3b-base-2601" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kakaocorp/kanana-2-30b-a3b-base-2601",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kakaocorp/kanana-2-30b-a3b-base-2601 with Docker Model Runner:
```
docker model run hf.co/kakaocorp/kanana-2-30b-a3b-base-2601
```

kanana-2-30b-a3b-base-2601 / model-00013-of-00013.safetensors

ryan-u

Upload folder using huggingface_hub

5672127 verified 5 months ago

Download

History Blame Contribute Delete

1.38 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: a493ac67832fdeba6a2d95a9598d2e306a09037d37977fccb2502154a2532dbb
Size of remote file:: 1.38 GB
SHA256:: eb061b51367004349962ea6436bd93435c803b5dd0db5410c8aad31b5abc2006

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.