nsfw

explicit

unaligned

ERP

Erotic

Model card Files Files and versions

xet

Community

Instructions to use Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers

How to use Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic with Transformers:

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

pipe = pipeline("text-generation", model="Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic

SGLang

How to use Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic 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 "Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic" \
    --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": "Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic",
		"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 "Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic" \
        --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": "Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/Sinensis/Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic
```

Broken-Tutu-24B-Unslop-v2.0-FP8-Dynamic / model-00003-of-00006.safetensors

Sinensis

Upload folder using huggingface_hub

ebbbed6 verified 10 months ago

download

history blame contribute delete

4.98 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:: 00d1e9be519c8a9da6b04a3a59b12142caf971b4eba38133339cbf11b1db3b7c
Size of remote file:: 4.98 GB
SHA256:: 82cfbc7ea085e6b74577c3284da13016a9182d51e610d4d1772dc29de8579755

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