Instructions to use Alignment-Lab-AI/ABS-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Alignment-Lab-AI/ABS-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "Alignment-Lab-AI/ABS-adapter") - Notebooks
- Google Colab
- Kaggle
| base_model: unsloth/Meta-Llama-3.1-8B | |
| library_name: peft | |
| license: llama3.1 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: outputs/out/qlora-llama3_1-8b | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) | |
| <details><summary>See axolotl config</summary> | |
| axolotl version: `0.4.1` | |
| ```yaml | |
| base_model: unsloth/Meta-Llama-3.1-8B | |
| tokenizer_type: AutoTokenizer | |
| #load_in_8bit: true | |
| load_in_4bit: true | |
| strict: false | |
| datasets: | |
| - path: Alignment-Lab-AI/claudeopus-sharegpt | |
| type: sharegpt | |
| chat_template: llama3 | |
| dataset_prepared_path: last_run_prepared | |
| val_set_size: 0.0 | |
| output_dir: ./outputs/out/qlora-llama3_1-8b | |
| save_safetensors: true | |
| adapter: qlora | |
| sequence_len: 8192 | |
| sample_packing: true | |
| #pad_to_sequence_len: true | |
| lora_r: 16 | |
| lora_alpha: 64 | |
| lora_dropout: 0.05 | |
| lora_target_modules: | |
| lora_target_linear: true | |
| gradient_accumulation_steps: 16 | |
| micro_batch_size: 2 | |
| num_epochs: 2 | |
| optimizer: adamw_torch | |
| lr_scheduler: cosine | |
| learning_rate: 0.00035 | |
| train_on_inputs: false | |
| group_by_length: true | |
| bf16: true | |
| tf32: true | |
| eval_sample_packing: true | |
| pad_to_sequence_len: true | |
| wandb_project: ARBIUS-8b | |
| gradient_checkpointing: true | |
| gradient_checkpointing_kwargs: | |
| use_reentrant: true | |
| logging_steps: 1 | |
| flash_attention: true | |
| neft_tune_alpha: 3 | |
| warmup_ratio: 0.5 | |
| evals_per_epoch: 4 | |
| saves_per_epoch: 1 | |
| weight_decay: 0.0 | |
| fsdp: | |
| - full_shard | |
| - auto_wrap | |
| fsdp_config: | |
| fsdp_limit_all_gathers: true | |
| fsdp_sync_module_states: true | |
| fsdp_offload_params: true | |
| fsdp_use_orig_params: false | |
| fsdp_cpu_ram_efficient_loading: true | |
| fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP | |
| fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer | |
| fsdp_state_dict_type: FULL_STATE_DICT | |
| fsdp_sharding_strategy: FULL_SHARD | |
| special_tokens: | |
| pad_token: <|end_of_text|> | |
| bos_token: <|begin_of_text|> | |
| eos_token: <|eot_id|> | |
| ``` | |
| </details><br> | |
| # outputs/out/qlora-llama3_1-8b | |
| This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the None dataset. | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.00035 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 6 | |
| - gradient_accumulation_steps: 16 | |
| - total_train_batch_size: 192 | |
| - total_eval_batch_size: 12 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 34 | |
| - num_epochs: 2 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.12.0 | |
| - Transformers 4.44.0 | |
| - Pytorch 2.1.2+cu118 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 |