Instructions to use sunshine-lwt/TokenPacker-HD-13b-9patch-144token with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sunshine-lwt/TokenPacker-HD-13b-9patch-144token with Transformers:

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

pipe = pipeline("text-generation", model="sunshine-lwt/TokenPacker-HD-13b-9patch-144token")

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

processor = AutoProcessor.from_pretrained("sunshine-lwt/TokenPacker-HD-13b-9patch-144token")
model = AutoModelForCausalLM.from_pretrained("sunshine-lwt/TokenPacker-HD-13b-9patch-144token", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sunshine-lwt/TokenPacker-HD-13b-9patch-144token with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sunshine-lwt/TokenPacker-HD-13b-9patch-144token"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sunshine-lwt/TokenPacker-HD-13b-9patch-144token",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sunshine-lwt/TokenPacker-HD-13b-9patch-144token

SGLang

How to use sunshine-lwt/TokenPacker-HD-13b-9patch-144token 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 "sunshine-lwt/TokenPacker-HD-13b-9patch-144token" \
    --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": "sunshine-lwt/TokenPacker-HD-13b-9patch-144token",
		"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 "sunshine-lwt/TokenPacker-HD-13b-9patch-144token" \
        --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": "sunshine-lwt/TokenPacker-HD-13b-9patch-144token",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sunshine-lwt/TokenPacker-HD-13b-9patch-144token with Docker Model Runner:
```
docker model run hf.co/sunshine-lwt/TokenPacker-HD-13b-9patch-144token
```

TokenPacker-HD-13b-9patch-144token / pytorch_model-00002-of-00003.bin

sunshine-lwt

upload

c3385ae verified about 2 years ago

History Blame Contribute Delete

9.9 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:: 1622597d9b46a8649fcba5fc0229081d8e573b3e4e4d938927deb32f1428a5f2
Size of remote file:: 9.9 GB
SHA256:: 24f3557cf5cdc2f6dfcffa5d3f50b9479a4d3654c02d08c25fa64f913ea5cf88

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

sunshine-lwt
/

TokenPacker-HD-13b-9patch-144token

Detected Pickle imports (3)

Xet Pointer Details