Instructions to use harmtech/Stheno-1.10-L2-13B-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use harmtech/Stheno-1.10-L2-13B-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="harmtech/Stheno-1.10-L2-13B-GPTQ")

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

tokenizer = AutoTokenizer.from_pretrained("harmtech/Stheno-1.10-L2-13B-GPTQ")
model = AutoModelForCausalLM.from_pretrained("harmtech/Stheno-1.10-L2-13B-GPTQ", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use harmtech/Stheno-1.10-L2-13B-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harmtech/Stheno-1.10-L2-13B-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harmtech/Stheno-1.10-L2-13B-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/harmtech/Stheno-1.10-L2-13B-GPTQ

SGLang

How to use harmtech/Stheno-1.10-L2-13B-GPTQ 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 "harmtech/Stheno-1.10-L2-13B-GPTQ" \
    --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": "harmtech/Stheno-1.10-L2-13B-GPTQ",
		"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 "harmtech/Stheno-1.10-L2-13B-GPTQ" \
        --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": "harmtech/Stheno-1.10-L2-13B-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use harmtech/Stheno-1.10-L2-13B-GPTQ with Docker Model Runner:
```
docker model run hf.co/harmtech/Stheno-1.10-L2-13B-GPTQ
```

Good stuff!

by Razrien - opened Jan 10, 2024

Discussion

Razrien

Jan 10, 2024

Works very well, even at the lower temperatures I usually use. Also, it's pretty darn quick!
I'm still not sure how various LLM software processes models, but this one is on the speedy end.
It's pretty flexible, able to figure out many odd scenarios that you may throw at it. I haven't tried it in a purley 'storytelling' scenario, (I mostly use these things for more.. entertaining puposes) but it's a welcome addition to my rather large NSFW toolbelt of models.

harmtech

Owner Jan 11, 2024

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