Instructions to use SystemAdmin123/test-repo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SystemAdmin123/test-repo 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, "SystemAdmin123/test-repo") - Notebooks
- Google Colab
- Kaggle
Download 2593284e-8d2b-49a4-9d90-a5407a2dda74.yml from SystemAdmin123/test-repo: direct link, hf CLI and curl.
- Browser
- Download file 1.35 kB
-
https://huggingface.co/SystemAdmin123/test-repo/resolve/d081ecdf0115041e8cf5064b79dd0e130b7906d3/2593284e-8d2b-49a4-9d90-a5407a2dda74.yml
- Command line
-
hf download hf://SystemAdmin123/test-repo@d081ecdf0115041e8cf5064b79dd0e130b7906d3/2593284e-8d2b-49a4-9d90-a5407a2dda74.yml
-
curl -L -o 2593284e-8d2b-49a4-9d90-a5407a2dda74.yml https://huggingface.co/SystemAdmin123/test-repo/resolve/d081ecdf0115041e8cf5064b79dd0e130b7906d3/2593284e-8d2b-49a4-9d90-a5407a2dda74.yml
1.35 kB
| base_model: lmsys/vicuna-7b-v1.3 | |
| batch_size: 32 | |
| bf16: true | |
| chat_template: tokenizer_default_fallback_alpaca | |
| datasets: | |
| - data_files: | |
| - 035ee0afc5220cd9_train_data.json | |
| ds_type: json | |
| format: custom | |
| path: /workspace/input_data/035ee0afc5220cd9_train_data.json | |
| type: | |
| field_input: algo_name | |
| field_instruction: question | |
| field_output: answer | |
| format: '{instruction} {input}' | |
| no_input_format: '{instruction}' | |
| system_format: '{system}' | |
| system_prompt: '' | |
| eval_steps: 20 | |
| flash_attention: true | |
| gpu_memory_limit: 80GiB | |
| gradient_checkpointing: true | |
| group_by_length: true | |
| hub_model_id: SystemAdmin123/e3a35688-e9b7-46d1-8f93-172a66f78e04 | |
| hub_strategy: checkpoint | |
| learning_rate: 0.0002 | |
| logging_steps: 10 | |
| lr_scheduler: cosine | |
| micro_batch_size: 2 | |
| model_type: AutoModelForCausalLM | |
| num_epochs: 10 | |
| optimizer: adamw_bnb_8bit | |
| output_dir: /workspace/axolotl/configs | |
| pad_to_sequence_len: true | |
| resize_token_embeddings_to_32x: false | |
| sample_packing: false | |
| save_steps: 40 | |
| save_total_limit: 1 | |
| sequence_len: 2048 | |
| tokenizer_type: LlamaTokenizerFast | |
| train_on_inputs: false | |
| trust_remote_code: true | |
| val_set_size: 0.1 | |
| wandb_entity: '' | |
| wandb_mode: online | |
| wandb_name: lmsys/vicuna-7b-v1.3-/tmp/035ee0afc5220cd9_train_data.json | |
| wandb_project: Gradients-On-Demand | |
| wandb_run: your_name | |
| wandb_runid: default | |
| warmup_ratio: 0.05 | |
| xformers_attention: true | |