Instructions to use explosion-testing/falcon-new-decoder-alibi-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use explosion-testing/falcon-new-decoder-alibi-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="explosion-testing/falcon-new-decoder-alibi-test")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("explosion-testing/falcon-new-decoder-alibi-test") model = AutoModelForCausalLM.from_pretrained("explosion-testing/falcon-new-decoder-alibi-test") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use explosion-testing/falcon-new-decoder-alibi-test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "explosion-testing/falcon-new-decoder-alibi-test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "explosion-testing/falcon-new-decoder-alibi-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/explosion-testing/falcon-new-decoder-alibi-test
- SGLang
How to use explosion-testing/falcon-new-decoder-alibi-test 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 "explosion-testing/falcon-new-decoder-alibi-test" \ --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": "explosion-testing/falcon-new-decoder-alibi-test", "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 "explosion-testing/falcon-new-decoder-alibi-test" \ --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": "explosion-testing/falcon-new-decoder-alibi-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use explosion-testing/falcon-new-decoder-alibi-test with Docker Model Runner:
docker model run hf.co/explosion-testing/falcon-new-decoder-alibi-test
| { | |
| "alibi": true, | |
| "architectures": [ | |
| "FalconForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "bias": false, | |
| "bos_token_id": 11, | |
| "eos_token_id": 11, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 64, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "falcon", | |
| "multi_query": true, | |
| "new_decoder_architecture": true, | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 2, | |
| "num_kv_heads": 2, | |
| "parallel_attn": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.31.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 1024 | |
| } | |