Instructions to use ddh0/OrcaMaid-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ddh0/OrcaMaid-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ddh0/OrcaMaid-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ddh0/OrcaMaid-13b") model = AutoModelForCausalLM.from_pretrained("ddh0/OrcaMaid-13b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ddh0/OrcaMaid-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ddh0/OrcaMaid-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ddh0/OrcaMaid-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ddh0/OrcaMaid-13b
- SGLang
How to use ddh0/OrcaMaid-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 "ddh0/OrcaMaid-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": "ddh0/OrcaMaid-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 "ddh0/OrcaMaid-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": "ddh0/OrcaMaid-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ddh0/OrcaMaid-13b with Docker Model Runner:
docker model run hf.co/ddh0/OrcaMaid-13b
OrcaMaid-13b
This is a merge of Microsoft's Orca-2-13b and Undi and IkariDev's Noromaid-v0.1.1-13b, with just a touch of Kal'tsit's cat-v1.0 mixed in.
The model recipe was as follows:
- Linear merge of Orca-2-13b @0.8 and cat-v1.0-13b @0.2 = OrcaCat-13b (no plans to release)
- Gradient SLERP merge of Noromaid-v0.1.1 @0.5 and OrcaCat-13b @0.5 = OrcaMaid-13b
Both merges were done in FP32 rather than FP16, due to Orca being released as FP32. I didn't want to risk losing any precision.
The overall goal of this merge is to create a model that sounds uniquely human and natural, without sacrificing intelligence. Edit: after some feedback from a few others, ranking on the Ayumi leaderboards, and more of my own testing, I believe I have succeeded as well as I reasonably could have hoped.
The prompt format is Alpaca. You can use the standard format as shown, but for best results, you should customize the system prompt to your specific needs.
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{YOUR MESSAGE HERE}
### Response:
{BOT MESSAGE HERE}
Misc. information
- BOS token is
<s> - EOS token is
</s> - Native context length is
4096 - Base model is Llama 2
- Due to the inclusion of Orca-2-13b, the model is subject to the terms of the Microsoft Research License
Thanks
- Thanks to Charles Goddard for his kind help with mergekit
- Thanks to Undi and IkariDev for Noromaid
- Thanks to Kal'tsit for Cat. See her original reddit post: Cat 1.0 is an uncensored, rp model aligned to be useful in all (even spicy)situations
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