Instructions to use openGPT-X/Teuken-7B-instruct-research-v0.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openGPT-X/Teuken-7B-instruct-research-v0.4 with Transformers:

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

pipe = pipeline("text-generation", model="openGPT-X/Teuken-7B-instruct-research-v0.4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openGPT-X/Teuken-7B-instruct-research-v0.4")
model = AutoModelForCausalLM.from_pretrained("openGPT-X/Teuken-7B-instruct-research-v0.4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openGPT-X/Teuken-7B-instruct-research-v0.4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openGPT-X/Teuken-7B-instruct-research-v0.4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openGPT-X/Teuken-7B-instruct-research-v0.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openGPT-X/Teuken-7B-instruct-research-v0.4

SGLang

How to use openGPT-X/Teuken-7B-instruct-research-v0.4 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 "openGPT-X/Teuken-7B-instruct-research-v0.4" \
    --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": "openGPT-X/Teuken-7B-instruct-research-v0.4",
		"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 "openGPT-X/Teuken-7B-instruct-research-v0.4" \
        --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": "openGPT-X/Teuken-7B-instruct-research-v0.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openGPT-X/Teuken-7B-instruct-research-v0.4 with Docker Model Runner:
```
docker model run hf.co/openGPT-X/Teuken-7B-instruct-research-v0.4
```

mfromm commited on Jan 7, 2025

Commit

1e97164

verified ·

1 Parent(s): 413f42a

Update README.md

Browse files

Files changed (1) hide show

README.md +0 -1

README.md CHANGED Viewed

@@ -186,7 +186,6 @@ print(f"Assistant: {outputs[0].outputs[0].text}")
 [Teuken-7B-instruct-research-v0.4](https://huggingface.co/openGPT-X/Teuken-7B-instruct-research-v0.4) was pre-trained on 4 trillion tokens of data from publicly available sources.
 The pretraining data has a cutoff of September 2023.
-More information is available in our preprint ["Data Processing for the OpenGPT-X Model Family"](http://arxiv.org/abs/2410.08800).
 ### Instruction-Tuning Data

 [Teuken-7B-instruct-research-v0.4](https://huggingface.co/openGPT-X/Teuken-7B-instruct-research-v0.4) was pre-trained on 4 trillion tokens of data from publicly available sources.
 The pretraining data has a cutoff of September 2023.
 ### Instruction-Tuning Data