Instructions to use Henk717/chronoboros-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Henk717/chronoboros-33B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Henk717/chronoboros-33B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Henk717/chronoboros-33B") model = AutoModelForCausalLM.from_pretrained("Henk717/chronoboros-33B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Henk717/chronoboros-33B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Henk717/chronoboros-33B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Henk717/chronoboros-33B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Henk717/chronoboros-33B
- SGLang
How to use Henk717/chronoboros-33B 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 "Henk717/chronoboros-33B" \ --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": "Henk717/chronoboros-33B", "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 "Henk717/chronoboros-33B" \ --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": "Henk717/chronoboros-33B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Henk717/chronoboros-33B with Docker Model Runner:
docker model run hf.co/Henk717/chronoboros-33B
Any plans for a superhot mix?
#1
by Delcos - opened
Any plans for this model similar to TheBloke and giving the model 8k context length?
Potentially but i'd need 8K context trained models using the same scaling for both origin models.
I also did not want to dilute it using an upstream model that was already merged with Superhot, so it would have to be finetunes done from scratch at 8K.