Instructions to use Henk717/airochronos-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Henk717/airochronos-33B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Henk717/airochronos-33B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Henk717/airochronos-33B") model = AutoModelForCausalLM.from_pretrained("Henk717/airochronos-33B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Henk717/airochronos-33B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Henk717/airochronos-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/airochronos-33B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Henk717/airochronos-33B
- SGLang
How to use Henk717/airochronos-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/airochronos-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/airochronos-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/airochronos-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/airochronos-33B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Henk717/airochronos-33B with Docker Model Runner:
docker model run hf.co/Henk717/airochronos-33B
Why weight avg instead of lora merge?
Airoboros is a qlora, why not just merge the lora into chronos?
Partially the tools I am familair with and me not noticing the qlora. But in this case the merge ratio is 75% chronos. So it is not just applying the lora. Its applying varing percentages and settling on one I liked.
Partially the tools I am familair with and me not noticing the qlora. But in this case the merge ratio is 75% chronos. So it is not just applying the lora. Its applying varing percentages and settling on one I liked.
Hijacking this thread a bit, but speaking of merging models can you share your method or script how you accomplished this? I'm trying to do something similar and just can't get it working right for some reason.
Scripts are here : https://github.com/ontocord/MDEL/tree/main/Model%20Merge%20And%20Analysis%20Tools
For this model I used the Enhanced Merger not the more advanced ones that let you do individual layers. Script variable was edited to merge it with 0.75, airoboros was selected as the first model.
Scripts are here : https://github.com/ontocord/MDEL/tree/main/Model%20Merge%20And%20Analysis%20Tools
For this model I used the Enhanced Merger not the more advanced ones that let you do individual layers. Script variable was edited to merge it with 0.75, airoboros was selected as the first model.
Thank you, that is very helpful.