Instructions to use Krauser/13B_UNC_LLam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Krauser/13B_UNC_LLam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Krauser/13B_UNC_LLam")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Krauser/13B_UNC_LLam") model = AutoModelForCausalLM.from_pretrained("Krauser/13B_UNC_LLam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Krauser/13B_UNC_LLam with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Krauser/13B_UNC_LLam" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Krauser/13B_UNC_LLam", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Krauser/13B_UNC_LLam
- SGLang
How to use Krauser/13B_UNC_LLam 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 "Krauser/13B_UNC_LLam" \ --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": "Krauser/13B_UNC_LLam", "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 "Krauser/13B_UNC_LLam" \ --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": "Krauser/13B_UNC_LLam", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Krauser/13B_UNC_LLam with Docker Model Runner:
docker model run hf.co/Krauser/13B_UNC_LLam
Download training_args.bin from Krauser/13B_UNC_LLam: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/Krauser/13B_UNC_LLam/resolve/main/training_args.bin
- Command line
-
hf download hf://Krauser/13B_UNC_LLam/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Krauser/13B_UNC_LLam/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 48acd1716c890b4b3a60f9ccc8c2e6ca03b9a677438e36fdb091e95a2bd9b0b0
- Size of remote file:
- 4.98 kB
- SHA256:
- 4abc2ce4962a0fed1d8dc7fcb820d0167c772ee381192c2e81e056ade92d3479
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