Instructions to use royallab/Aetheria-L2-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use royallab/Aetheria-L2-70B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="royallab/Aetheria-L2-70B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("royallab/Aetheria-L2-70B") model = AutoModelForCausalLM.from_pretrained("royallab/Aetheria-L2-70B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use royallab/Aetheria-L2-70B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "royallab/Aetheria-L2-70B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "royallab/Aetheria-L2-70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/royallab/Aetheria-L2-70B
- SGLang
How to use royallab/Aetheria-L2-70B 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 "royallab/Aetheria-L2-70B" \ --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": "royallab/Aetheria-L2-70B", "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 "royallab/Aetheria-L2-70B" \ --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": "royallab/Aetheria-L2-70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use royallab/Aetheria-L2-70B with Docker Model Runner:
docker model run hf.co/royallab/Aetheria-L2-70B
Aetheria-L2-70B
This is a Llama 2-based model consisting of a merge between:
- Sao10K/Euryale-1.3-L2-70B
- allenai/tulu-2-dpo-70b
- GOAT-AI/GOAT-70B-Storytelling
- Doctor-Shotgun/limarpv3-llama2-70b-qlora
This model combines the excellent Euryale v1.3 base with the DPO training of the Tulu v2 model and creative prose training of the GOAT Storytelling model. The LimaRP v3 qlora was then added for further roleplaying capability and the ability to tune the length of the outputs.
The goal was to create a capable 70B model for collaborative storytelling and roleplay.
Usage:
The intended prompt format is the Alpaca instruction format of LimaRP v3:
### Instruction:
Character's Persona: {bot character description}
User's Persona: {user character description}
Scenario: {what happens in the story}
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User.
### Input:
User: {utterance}
### Response:
Character: {utterance}
### Input
User: {utterance}
### Response:
Character: {utterance}
(etc.)
Message length control
Due to the inclusion of LimaRP v3, it is possible to append a length modifier to the response instruction sequence, like this:
### Input
User: {utterance}
### Response: (length = medium)
Character: {utterance}
This has an immediately noticeable effect on bot responses. The available lengths are: micro, tiny, short, medium, long, massive, huge, enormous, humongous, unlimited. The recommended starting length is medium. Keep in mind that the AI may ramble or impersonate the user with very long messages.
Bias, Risks, and Limitations
The model will show biases similar to those observed in niche roleplaying forums on the Internet, besides those exhibited by the base model. It is not intended for supplying factual information or advice in any form.
Training Details
This model is a merge. Please refer to the link repositories of the merged models for details.
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