Text Generation
Transformers
Safetensors
Korean
llama
meta
llama-2
llama-2-ko
text-generation-inference
Instructions to use URP/urllm-ko-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use URP/urllm-ko-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="URP/urllm-ko-7b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("URP/urllm-ko-7b") model = AutoModelForCausalLM.from_pretrained("URP/urllm-ko-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use URP/urllm-ko-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "URP/urllm-ko-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "URP/urllm-ko-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/URP/urllm-ko-7b
- SGLang
How to use URP/urllm-ko-7b 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 "URP/urllm-ko-7b" \ --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": "URP/urllm-ko-7b", "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 "URP/urllm-ko-7b" \ --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": "URP/urllm-ko-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use URP/urllm-ko-7b with Docker Model Runner:
docker model run hf.co/URP/urllm-ko-7b
Download tokenizer.model from URP/urllm-ko-7b: direct link, hf CLI and curl.
- Browser
- Download file 866 kB
-
https://huggingface.co/URP/urllm-ko-7b/resolve/main/tokenizer.model
- Command line
-
hf download hf://URP/urllm-ko-7b/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/URP/urllm-ko-7b/resolve/main/tokenizer.model
866 kB
- Xet hash:
- d73f8d9c991f549e402a977e6b273fdf5ec1005d61639a84475033d06c08d2ba
- Size of remote file:
- 866 kB
- SHA256:
- efc4d7f9ad5cb93f27d7ceddf7089e6205a0b49b8513bdd9f3f95ecc06f06574
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.