Instructions to use larryvrh/Yi-34B-200K-Llamafied with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use larryvrh/Yi-34B-200K-Llamafied with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="larryvrh/Yi-34B-200K-Llamafied")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("larryvrh/Yi-34B-200K-Llamafied") model = AutoModelForCausalLM.from_pretrained("larryvrh/Yi-34B-200K-Llamafied") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use larryvrh/Yi-34B-200K-Llamafied with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "larryvrh/Yi-34B-200K-Llamafied" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "larryvrh/Yi-34B-200K-Llamafied", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/larryvrh/Yi-34B-200K-Llamafied
- SGLang
How to use larryvrh/Yi-34B-200K-Llamafied 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 "larryvrh/Yi-34B-200K-Llamafied" \ --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": "larryvrh/Yi-34B-200K-Llamafied", "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 "larryvrh/Yi-34B-200K-Llamafied" \ --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": "larryvrh/Yi-34B-200K-Llamafied", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use larryvrh/Yi-34B-200K-Llamafied with Docker Model Runner:
docker model run hf.co/larryvrh/Yi-34B-200K-Llamafied
- Xet hash:
- 7cd7606ad687008a7880e4131a73c74e16580949907a8f414b5763b93c2d191d
- Size of remote file:
- 9.94 GB
- SHA256:
- a3ad53ee6c607d9a0be1b08c4110ec689576daafbb5a635dd58ea4ae44fc9391
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.