Instructions to use sn56a6/edf1df8b-bf6c-4fc0-96e6-8e191e0da18f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sn56a6/edf1df8b-bf6c-4fc0-96e6-8e191e0da18f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("peft-internal-testing/tiny-dummy-qwen2") model = PeftModel.from_pretrained(base_model, "sn56a6/edf1df8b-bf6c-4fc0-96e6-8e191e0da18f") - Notebooks
- Google Colab
- Kaggle
Download adapter_config.json from sn56a6/edf1df8b-bf6c-4fc0-96e6-8e191e0da18f: direct link, hf CLI and curl.
- Browser
- Download file 741 Bytes
-
https://huggingface.co/sn56a6/edf1df8b-bf6c-4fc0-96e6-8e191e0da18f/resolve/main/adapter_config.json
- Command line
-
hf download hf://sn56a6/edf1df8b-bf6c-4fc0-96e6-8e191e0da18f/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/sn56a6/edf1df8b-bf6c-4fc0-96e6-8e191e0da18f/resolve/main/adapter_config.json
741 Bytes
| { | |
| "alpha_pattern": {}, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "peft-internal-testing/tiny-dummy-qwen2", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 128, | |
| "lora_dropout": 0.1, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "r": 64, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "up_proj", | |
| "q_proj", | |
| "o_proj", | |
| "k_proj", | |
| "gate_proj", | |
| "v_proj", | |
| "down_proj" | |
| ], | |
| "task_type": "CAUSAL_LM", | |
| "use_dora": false, | |
| "use_rslora": false | |
| } |