Instructions to use caisarl76/Mistral-7B-OpenOrca-Guanaco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use caisarl76/Mistral-7B-OpenOrca-Guanaco with Transformers:

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

pipe = pipeline("text-generation", model="caisarl76/Mistral-7B-OpenOrca-Guanaco")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("caisarl76/Mistral-7B-OpenOrca-Guanaco")
model = AutoModelForCausalLM.from_pretrained("caisarl76/Mistral-7B-OpenOrca-Guanaco")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use caisarl76/Mistral-7B-OpenOrca-Guanaco with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "caisarl76/Mistral-7B-OpenOrca-Guanaco"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caisarl76/Mistral-7B-OpenOrca-Guanaco",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/caisarl76/Mistral-7B-OpenOrca-Guanaco

SGLang

How to use caisarl76/Mistral-7B-OpenOrca-Guanaco 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 "caisarl76/Mistral-7B-OpenOrca-Guanaco" \
    --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": "caisarl76/Mistral-7B-OpenOrca-Guanaco",
		"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 "caisarl76/Mistral-7B-OpenOrca-Guanaco" \
        --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": "caisarl76/Mistral-7B-OpenOrca-Guanaco",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use caisarl76/Mistral-7B-OpenOrca-Guanaco with Docker Model Runner:
```
docker model run hf.co/caisarl76/Mistral-7B-OpenOrca-Guanaco
```

Model Details

Developed by: Minds And Company
Backbone Model: Mistral-7B-OpenOrca
Library: HuggingFace Transformers

Dataset Details

Used Datasets

Alpaca-style dataset

Prompt Template

Llama Prompt Template

Limitations & Biases:

Llama2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.

Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/

License Disclaimer:

This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.

Contact Us

Minds And Company

Citiation:

Please kindly cite using the following BibTeX:


@software{lian2023mistralorca1
  title = {MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
  author = {Wing Lian and Bleys Goodson and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca},
}

@misc{mukherjee2023orca,
      title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, 
      author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
      year={2023},
      eprint={2306.02707},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{Orca-best,
  title = {Orca-best: A filtered version of orca gpt4 dataset.},
  author = {Shahul Es},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://huggingface.co/datasets/shahules786/orca-best/},
}

Readme format: Riiid/sheep-duck-llama-2-70b-v1.1

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Dataset used to train caisarl76/Mistral-7B-OpenOrca-Guanaco

Paper for caisarl76/Mistral-7B-OpenOrca-Guanaco

Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Paper • 2306.02707 • Published Jun 5, 2023 • 51