Instructions to use TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ

SGLang

How to use TheBloke/OpenOrcaxOpenChat-Preview2-13B-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 "TheBloke/OpenOrcaxOpenChat-Preview2-13B-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": "TheBloke/OpenOrcaxOpenChat-Preview2-13B-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 "TheBloke/OpenOrcaxOpenChat-Preview2-13B-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": "TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ
```

EOS Token

by ssmi153 - opened Aug 4, 2023

Discussion

ssmi153

Aug 4, 2023

•

edited Aug 4, 2023

I'm finding that these quantized models don't know how to stop. Comparing your special_tokens_map.json to the original repo, you have a different "eos_token" value. I think this should be "<|end_of_turn|>" rather than "</s>".

TheBloke

Owner Aug 4, 2023

Ah yeah, they must have edited their JSON after I did my quantisations. I have edited my JSON to match theirs, so do a re-download of those files and hopefully that will sort it out.

countzero238

Aug 5, 2023

Could it be that TheBloke/vicuna-13B-v1.5-16K-GPTQ suffers from the same problem?

TheBloke

Owner Aug 5, 2023

Could it be that TheBloke/vicuna-13B-v1.5-16K-GPTQ suffers from the same problem?

Don't think so - Vicuna has always used </s> and this hasn't changed

countzero238

Aug 5, 2023

Thanks for the answer! Your newer GPTQ-models don't know when to stop in 1 of five cases on my end. The original vicuna-13B-v1.5-16K seems to be fine though..

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