Text Generation
Transformers
Safetensors
English
gemma
LLM training
SLM
technique
pretrain
text-generation-inference
Instructions to use Finisha-F-scratch/Expedia-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Finisha-F-scratch/Expedia-LLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Finisha-F-scratch/Expedia-LLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Finisha-F-scratch/Expedia-LLM") model = AutoModelForCausalLM.from_pretrained("Finisha-F-scratch/Expedia-LLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Finisha-F-scratch/Expedia-LLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Finisha-F-scratch/Expedia-LLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Finisha-F-scratch/Expedia-LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Finisha-F-scratch/Expedia-LLM
- SGLang
How to use Finisha-F-scratch/Expedia-LLM 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 "Finisha-F-scratch/Expedia-LLM" \ --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": "Finisha-F-scratch/Expedia-LLM", "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 "Finisha-F-scratch/Expedia-LLM" \ --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": "Finisha-F-scratch/Expedia-LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Finisha-F-scratch/Expedia-LLM with Docker Model Runner:
docker model run hf.co/Finisha-F-scratch/Expedia-LLM
- Xet hash:
- bbbb7f4c413658fd73f2b76851c5f9b7aa77ea97d038bcefe56a4643512ae25e
- Size of remote file:
- 426 MB
- SHA256:
- 6e4277f464f6a535b48f5a8fa02ebca2821f6233ef55c302e6c3a9283085ed82
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