Instructions to use TeeZee/Orca-2-13b_flat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TeeZee/Orca-2-13b_flat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TeeZee/Orca-2-13b_flat")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TeeZee/Orca-2-13b_flat") model = AutoModelForCausalLM.from_pretrained("TeeZee/Orca-2-13b_flat") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TeeZee/Orca-2-13b_flat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TeeZee/Orca-2-13b_flat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TeeZee/Orca-2-13b_flat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TeeZee/Orca-2-13b_flat
- SGLang
How to use TeeZee/Orca-2-13b_flat 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 "TeeZee/Orca-2-13b_flat" \ --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": "TeeZee/Orca-2-13b_flat", "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 "TeeZee/Orca-2-13b_flat" \ --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": "TeeZee/Orca-2-13b_flat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TeeZee/Orca-2-13b_flat with Docker Model Runner:
docker model run hf.co/TeeZee/Orca-2-13b_flat
Lots of good models use Orca for their merges, however vanilla Orca has vocabulary size of 32003, 3 last tokens are ChatML tokens and a PAD token. This causes errors during a merge with models with standard 32000 vocabulary size.
I've removed those tokens from volabulary and resized model embeddings to mach 32000 standard size. So this model is ready to be used as a merge component in mergekit. It may not work on its own with ChatML template anymore.
model.resize_token_embeddings(32000)
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