Text Generation
Transformers
PyTorch
llama
alpaca
vicuna
uncensored
cot
chain of thought
story
adventure
roleplay
rp
Merge
mix
instruct
wizardlm
superhot
supercot
manticore
hippogriff
text-generation-inference
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
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All datasets from all models and LoRAs used were documented and reviewed as model candidates for merging.
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Model candidates were based on five core principles: creativity, logic, inference, instruction following,
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and longevity of trained responses.
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This is not a reflection of ChanSung's excellent work - it merely did not fit the purpose of this model.
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## Language Models and LoRAs Used Credits:
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All datasets from all models and LoRAs used were documented and reviewed as model candidates for merging.
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Model candidates were based on five core principles: creativity, logic, inference, instruction following,
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and longevity of trained responses. SuperHOT-prototype30b-8192 was used in this mix, not the 8K version;
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the prototype LoRA seems to have been removed [from HF] as of this writing. The GPT4Alpaca LoRA from
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Chansung was removed from this amalgam following a thorough review of where censorship and railroading
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the user came from in 33B-Lazarus. This is not a reflection of ChanSung's excellent work - it merely did
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not fit the purpose of this model.
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## Language Models and LoRAs Used Credits:
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