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

Libraries

How to use Open-Orca/OpenOrcaxOpenChat-Preview2-13B with Transformers:

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Open-Orca/OpenOrcaxOpenChat-Preview2-13B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B

SGLang

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

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

bleysg commited on Aug 3, 2023

Commit

8a25aa3

1 Parent(s): ffa5716

Update README.md

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@@ -66,7 +66,7 @@ As well, we have evaluated using the methodology and tools for the HuggingFace L
 ## AGIEval Performance
 We present our results in two columns.
-The column for "`(Orca Paper eval`" uses the methods outlined in the Orca paper, so as to be a direct apples-to-apples comparison with the results from the paper.
 The column for "`(HF Leaderboard eval)`" uses EleutherAI's LM Evaluation Harness with settings outlined by HuggingFace. These results are not comparable to the other columns, as the methods are different.
 ![OpenOrca Preview2 AGIEval Performance](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaP2AGIEval.png "AGIEval Performance")
@@ -74,7 +74,7 @@ The column for "`(HF Leaderboard eval)`" uses EleutherAI's LM Evaluation Harness
 ## BigBench-Hard Performance
 We present our results in two columns.
-The column for "`(Orca Paper eval`" uses the methods outlined in the Orca paper, so as to be a direct apples-to-apples comparison with the results from the paper.
 The column for "`(HF Leaderboard eval)`" uses EleutherAI's LM Evaluation Harness with settings outlined by HuggingFace. These results are not comparable to the other columns, as the methods are different.
 ![OpenOrca Preview2 BigBench-Hard Performance](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaP2BigBenchHardEval.png "BigBench-Hard Performance")

 ## AGIEval Performance
 We present our results in two columns.
+The column for "`(Orca Paper eval)`" uses the methods outlined in the Orca paper, so as to be a direct apples-to-apples comparison with the results from the paper.
 The column for "`(HF Leaderboard eval)`" uses EleutherAI's LM Evaluation Harness with settings outlined by HuggingFace. These results are not comparable to the other columns, as the methods are different.
 ![OpenOrca Preview2 AGIEval Performance](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaP2AGIEval.png "AGIEval Performance")
 ## BigBench-Hard Performance
 We present our results in two columns.
+The column for "`(Orca Paper eval)`" uses the methods outlined in the Orca paper, so as to be a direct apples-to-apples comparison with the results from the paper.
 The column for "`(HF Leaderboard eval)`" uses EleutherAI's LM Evaluation Harness with settings outlined by HuggingFace. These results are not comparable to the other columns, as the methods are different.
 ![OpenOrca Preview2 BigBench-Hard Performance](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B/resolve/main/Images/OpenOrcaP2BigBenchHardEval.png "BigBench-Hard Performance")