Instructions to use GeorgiaTechResearchInstitute/galpaca-30b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GeorgiaTechResearchInstitute/galpaca-30b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="GeorgiaTechResearchInstitute/galpaca-30b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("GeorgiaTechResearchInstitute/galpaca-30b") model = AutoModelForCausalLM.from_pretrained("GeorgiaTechResearchInstitute/galpaca-30b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use GeorgiaTechResearchInstitute/galpaca-30b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GeorgiaTechResearchInstitute/galpaca-30b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GeorgiaTechResearchInstitute/galpaca-30b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/GeorgiaTechResearchInstitute/galpaca-30b
- SGLang
How to use GeorgiaTechResearchInstitute/galpaca-30b 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 "GeorgiaTechResearchInstitute/galpaca-30b" \ --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": "GeorgiaTechResearchInstitute/galpaca-30b", "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 "GeorgiaTechResearchInstitute/galpaca-30b" \ --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": "GeorgiaTechResearchInstitute/galpaca-30b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use GeorgiaTechResearchInstitute/galpaca-30b with Docker Model Runner:
docker model run hf.co/GeorgiaTechResearchInstitute/galpaca-30b
| { | |
| "_name_or_path": "./galpaca-30b", | |
| "_remove_final_layer_norm": false, | |
| "activation_dropout": 0.0, | |
| "activation_function": "gelu", | |
| "architectures": [ | |
| "OPTForCausalLM" | |
| ], | |
| "attention_dropout": 0.1, | |
| "bos_token_id": 0, | |
| "do_layer_norm_before": true, | |
| "dropout": 0.1, | |
| "enable_bias": true, | |
| "eos_token_id": 2, | |
| "ffn_dim": 28672, | |
| "hidden_size": 7168, | |
| "init_std": 0.02, | |
| "layer_norm_elementwise_affine": true, | |
| "layerdrop": 0.0, | |
| "learned_embeddings": true, | |
| "max_position_embeddings": 2048, | |
| "model_type": "opt", | |
| "num_attention_heads": 56, | |
| "num_hidden_layers": 48, | |
| "pad_token_id": 1, | |
| "scale_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.28.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 50000, | |
| "word_embed_proj_dim": 7168 | |
| } | |