Instructions to use Norquinal/PetrolOrca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Norquinal/PetrolOrca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Norquinal/PetrolOrca")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Norquinal/PetrolOrca") model = AutoModelForCausalLM.from_pretrained("Norquinal/PetrolOrca") - Inference
- Local Apps Settings
- vLLM
How to use Norquinal/PetrolOrca with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Norquinal/PetrolOrca" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Norquinal/PetrolOrca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Norquinal/PetrolOrca
- SGLang
How to use Norquinal/PetrolOrca 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 "Norquinal/PetrolOrca" \ --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": "Norquinal/PetrolOrca", "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 "Norquinal/PetrolOrca" \ --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": "Norquinal/PetrolOrca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Norquinal/PetrolOrca with Docker Model Runner:
docker model run hf.co/Norquinal/PetrolOrca
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README.md
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These samples were then back-filled using gpt-4/gpt-3.5-turbo-16k or otherwise converted to fit the prompt format.
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## Prompt Format
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The model
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style: roleplay
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[System note: Write at least two paragraphs. Be descriptive and immersive, providing vivid details about {{char}}'s actions, emotions, and the environment.]
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{{char}}:
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The third one seems to work the best. I would recommend experimenting with creating your own to best suit your needs.
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## Finetuing Parameters
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- LoRA Rank: 64
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- LoRA Alpha: 16
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- LoRA Dropout: 0.1
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- BF16 Training
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- Cutoff Length: 2048
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- Training Epoch(s): 2
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These samples were then back-filled using gpt-4/gpt-3.5-turbo-16k or otherwise converted to fit the prompt format.
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## Prompt Format
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The model uses the following prompt format:
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```
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---
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style: roleplay
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[System note: Write at least two paragraphs. Be descriptive and immersive, providing vivid details about {{char}}'s actions, emotions, and the environment.]
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{{char}}:
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```
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The third one seems to work the best. I would recommend experimenting with creating your own to best suit your needs.
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