Instructions to use CalderaAI/30B-Epsilon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CalderaAI/30B-Epsilon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CalderaAI/30B-Epsilon")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CalderaAI/30B-Epsilon") model = AutoModelForCausalLM.from_pretrained("CalderaAI/30B-Epsilon") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use CalderaAI/30B-Epsilon with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CalderaAI/30B-Epsilon" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CalderaAI/30B-Epsilon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CalderaAI/30B-Epsilon
- SGLang
How to use CalderaAI/30B-Epsilon 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 "CalderaAI/30B-Epsilon" \ --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": "CalderaAI/30B-Epsilon", "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 "CalderaAI/30B-Epsilon" \ --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": "CalderaAI/30B-Epsilon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CalderaAI/30B-Epsilon with Docker Model Runner:
docker model run hf.co/CalderaAI/30B-Epsilon
Is this the 'better than Lazarus' model?
" I hope you manage to find this model useful. I have an enormously better release than Laz coming. If you'd like, keep an eye out; it has no boundaries and is highly competent [33B-Epilogue is uploading...]."
Earlier this was teased on a Lazarus discussion by the author. Is this the model in question?
It is, it's now my go-to 30b for now. Model card is back-asswards as I made it public before adding that in ha. It's a complex merge based on best practices learned from previous experiments. Of course, results will be subjective for everyone.
It is, it's now my go-to 30b for now. Model card is back-asswards as I made it public before adding that in ha.
I suppose writing a full model card might take a while, but for the time being could you put down the prompt / instruction style?
Alpaca style prompting, its a merge of the following: [SuperCOT[superhot2-8k[(wizardlmuncensored[StoryV2(hippogriff+manticore)])]
(Source: Digitous himself in DM's)
Alpaca style prompting, its a merge of the following: [SuperCOT[superhot2-8k[(wizardlmuncensored[StoryV2(hippogriff+manticore)])]
(Source: Digitous himself in DM's)
Thanks for answering. So it's a superhot 8k model too?
So I can't use alpha on this and should use scaling of 4?
Alpaca style prompting, its a merge of the following: [SuperCOT[superhot2-8k[(wizardlmuncensored[StoryV2(hippogriff+manticore)])]
(Source: Digitous himself in DM's)Thanks for answering. So it's a superhot 8k model too?
Apologies for the wait - Ah, it's based on the old prototype of SuperHot - so no 8K yet; just updated the model card. Thank you for your patience.