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
```

What is the context length of this model?

by denrykhlov - opened Oct 3, 2023

Discussion

denrykhlov

Oct 3, 2023

subj

mirek190

Oct 3, 2023

32k as guff shows

hiiamsid

Oct 4, 2023

hello @mirek190 where did you find this information about 32K? I was also looking for mistral model with longer sequence length and found that it was actually trained on 8K token also on this specific model card it has mentioned 7B-8K. However mistral model has been implemented using sliding window approach due to which it considers tokens outside of the window as well while predicting next word but i was not able to find any evaluation or how to use it for 16k or 32K tokens

mirek190

Oct 4, 2023

I'm using llamacpp where models are using newest binary implementation called gguf.
As gguf has baked in model parameters during start you can check what parameters are loaded .
So after loaded the model I see ctx 32k.

bleysg

OpenOrca org Oct 5, 2023

It was trained with an 8192 token context window: https://huggingface.co/mistralai/Mistral-7B-v0.1/discussions/4

bleysg changed discussion status to closed Oct 5, 2023

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