Instructions to use Open-Orca/Mistral-7B-OpenOrca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Open-Orca/Mistral-7B-OpenOrca with Transformers:

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

pipe = pipeline("text-generation", model="Open-Orca/Mistral-7B-OpenOrca")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Open-Orca/Mistral-7B-OpenOrca")
model = AutoModelForCausalLM.from_pretrained("Open-Orca/Mistral-7B-OpenOrca")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Open-Orca/Mistral-7B-OpenOrca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Open-Orca/Mistral-7B-OpenOrca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Open-Orca/Mistral-7B-OpenOrca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Open-Orca/Mistral-7B-OpenOrca

SGLang

How to use Open-Orca/Mistral-7B-OpenOrca 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/Mistral-7B-OpenOrca" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Open-Orca/Mistral-7B-OpenOrca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/Mistral-7B-OpenOrca" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Open-Orca/Mistral-7B-OpenOrca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Open-Orca/Mistral-7B-OpenOrca with Docker Model Runner:
```
docker model run hf.co/Open-Orca/Mistral-7B-OpenOrca
```

bleysg commited on Oct 4, 2023

Commit

84afb23

1 Parent(s): 17bc883

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -2

README.md CHANGED Viewed

@@ -120,13 +120,19 @@ We compare our results to the base Mistral-7B model (using LM Evaluation Harness
 We find **129%** of the base model's performance on AGI Eval, averaging **0.397**.
 As well, we significantly improve upon the official `mistralai/Mistral-7B-Instruct-v0.1` finetuning, achieving **119%** of their performance.
-![OpenOrca-Platypus2-13B AGIEval Performance](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca/resolve/main/Images/MistralOrca7BAGIEval.png "AGIEval Performance")
 ## BigBench-Hard Performance
 We find **119%** of the base model's performance on BigBench-Hard, averaging **0.416**.
-![OpenOrca-Platypus2-13B BigBench-Hard Performance](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca/resolve/main/Images/MistralOrca7BBigBenchHard.png "BigBench-Hard Performance")
 # Dataset

 We find **129%** of the base model's performance on AGI Eval, averaging **0.397**.
 As well, we significantly improve upon the official `mistralai/Mistral-7B-Instruct-v0.1` finetuning, achieving **119%** of their performance.
+![AGIEval Performance](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca/resolve/main/Images/MistralOrca7BAGIEval.png "AGIEval Performance")
 ## BigBench-Hard Performance
 We find **119%** of the base model's performance on BigBench-Hard, averaging **0.416**.
+![BigBench-Hard Performance](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca/resolve/main/Images/MistralOrca7BBigBenchHard.png "BigBench-Hard Performance")
+## GPT4ALL Leaderboard Performance
+We gain a slight edge over our previous releases, again topping the leaderboard, averaging **72.38**.
+![GPT4ALL Performance](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca/resolve/main/Images/MistralOrca7BGPT4ALL.png "GPT4ALL Performance")
 # Dataset