Instructions to use bigcode/santacoder-fast-inference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigcode/santacoder-fast-inference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bigcode/santacoder-fast-inference")# Load model directly from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("bigcode/santacoder-fast-inference") model = AutoModelWithLMHead.from_pretrained("bigcode/santacoder-fast-inference") - Notebooks
- Google Colab
- Kaggle
| { | |
| "activation_function": "gelu_pytorch_tanh", | |
| "architectures": [ | |
| "GPTBigCodeLMHeadModel" | |
| ], | |
| "attention_softmax_in_fp32": true, | |
| "attention_type": 2, | |
| "attn_pdrop": 0.1, | |
| "bos_token_id": 50256, | |
| "embd_pdrop": 0.1, | |
| "eos_token_id": 50256, | |
| "inference_runner": 0, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "gpt_bigcode", | |
| "n_embd": 2048, | |
| "n_head": 16, | |
| "n_inner": 8192, | |
| "n_layer": 24, | |
| "n_positions": 2048, | |
| "resid_pdrop": 0.1, | |
| "runner_max_sequence_length": null, | |
| "scale_attention_softmax_in_fp32": true, | |
| "scale_attn_weights": true, | |
| "summary_activation": null, | |
| "summary_first_dropout": 0.1, | |
| "summary_proj_to_labels": true, | |
| "summary_type": "cls_index", | |
| "summary_use_proj": true, | |
| "transformers_version": "4.27.0.dev0", | |
| "use_cache": true, | |
| "validate_runner_input": true, | |
| "vocab_size": 49280 | |
| } | |