Instructions to use Billyyy/extended_llama_mongolian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Billyyy/extended_llama_mongolian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Billyyy/extended_llama_mongolian")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Billyyy/extended_llama_mongolian") model = AutoModelForCausalLM.from_pretrained("Billyyy/extended_llama_mongolian", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Billyyy/extended_llama_mongolian with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Billyyy/extended_llama_mongolian" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Billyyy/extended_llama_mongolian", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Billyyy/extended_llama_mongolian
- SGLang
How to use Billyyy/extended_llama_mongolian 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 "Billyyy/extended_llama_mongolian" \ --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": "Billyyy/extended_llama_mongolian", "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 "Billyyy/extended_llama_mongolian" \ --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": "Billyyy/extended_llama_mongolian", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Billyyy/extended_llama_mongolian with Docker Model Runner:
docker model run hf.co/Billyyy/extended_llama_mongolian
- Xet hash:
- c75db9746d9a0b6f597a210193feb981e78c395e7ffdd266dbd61f9ed5cea634
- Size of remote file:
- 218 MB
- SHA256:
- 03b7a9e155815dae77ac273f935e15d158790eda68bddf5d2dc0ba77c359bd22
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