Instructions to use adalib/beatnum-sub-cond-gen-codegen-350M-mono-prefix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adalib/beatnum-sub-cond-gen-codegen-350M-mono-prefix with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") model = PeftModel.from_pretrained(base_model, "adalib/beatnum-sub-cond-gen-codegen-350M-mono-prefix") - Notebooks
- Google Colab
- Kaggle
Download adapter_config.json from adalib/beatnum-sub-cond-gen-codegen-350M-mono-prefix: direct link, hf CLI and curl.
- Browser
- Download file 386 Bytes
-
https://huggingface.co/adalib/beatnum-sub-cond-gen-codegen-350M-mono-prefix/resolve/328b5a15ef96c97b73ebc9a4caa2e607eb16e129/adapter_config.json
- Command line
-
hf download hf://adalib/beatnum-sub-cond-gen-codegen-350M-mono-prefix@328b5a15ef96c97b73ebc9a4caa2e607eb16e129/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/adalib/beatnum-sub-cond-gen-codegen-350M-mono-prefix/resolve/328b5a15ef96c97b73ebc9a4caa2e607eb16e129/adapter_config.json
386 Bytes
| { | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "Salesforce/codegen-350M-mono", | |
| "encoder_hidden_size": 1024, | |
| "inference_mode": true, | |
| "num_attention_heads": 16, | |
| "num_layers": 20, | |
| "num_transformer_submodules": 1, | |
| "num_virtual_tokens": 5, | |
| "peft_type": "PREFIX_TUNING", | |
| "prefix_projection": false, | |
| "revision": null, | |
| "task_type": "CAUSAL_LM", | |
| "token_dim": 1024 | |
| } |