Instructions to use ddh0/OrcaMaid-v2-FIX-13b-32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ddh0/OrcaMaid-v2-FIX-13b-32k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ddh0/OrcaMaid-v2-FIX-13b-32k", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ddh0/OrcaMaid-v2-FIX-13b-32k", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("ddh0/OrcaMaid-v2-FIX-13b-32k", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ddh0/OrcaMaid-v2-FIX-13b-32k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ddh0/OrcaMaid-v2-FIX-13b-32k" # 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-v2-FIX-13b-32k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ddh0/OrcaMaid-v2-FIX-13b-32k
- SGLang
How to use ddh0/OrcaMaid-v2-FIX-13b-32k 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-v2-FIX-13b-32k" \ --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-v2-FIX-13b-32k", "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-v2-FIX-13b-32k" \ --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-v2-FIX-13b-32k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ddh0/OrcaMaid-v2-FIX-13b-32k with Docker Model Runner:
docker model run hf.co/ddh0/OrcaMaid-v2-FIX-13b-32k
OrcaMaid-13b-v2-FIX-32k
This is the fixed version of OrcaMaid-v2-13b, extended to 32768 context length via YaRN. The (now-deleted) v2 model had issues with the merged tokenizer that prevented it from stopping when necessary, and caused it to output broken ChatML tokens like <|im_end, etc.
This is a gradient SLERP merge of Microsoft's Orca-2-13b and Undi and IkariDev's Noromaid-v0.1.1-13b, biased towards Orca.
Just as with OrcaMaid v1, the overall goal of this merge is to create a model that sounds uniquely human and natural, without sacrificing intelligence.
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
32768via YaRN (original context length was4096) - 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 (as always)
- Thanks to Undi and IkariDev for Noromaid
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