Instructions to use SethGA/neocortex-grounded with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SethGA/neocortex-grounded with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "SethGA/neocortex-grounded") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| base_model: NousResearch/Llama-2-7b-hf | |
| model-index: | |
| - name: neocortex-grounded | |
| 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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) | |
| <details><summary>See axolotl config</summary> | |
| axolotl version: `0.4.0` | |
| ```yaml | |
| base_model: NousResearch/Llama-2-7b-hf | |
| model_type: LlamaForCausalLM | |
| tokenizer_type: LlamaTokenizer | |
| is_llama_derived_model: true | |
| hub_model_id: neocortex-grounded | |
| load_in_8bit: false | |
| load_in_4bit: true | |
| strict: false | |
| datasets: | |
| - path: SethGA/neocortex_grounded_23k | |
| type: alpaca | |
| shards: 20 | |
| dataset_prepared_path: | |
| val_set_size: 0.05 | |
| output_dir: ./qlora-out | |
| adapter: qlora | |
| lora_model_dir: | |
| sequence_len: 4096 | |
| sample_packing: false | |
| eval_sample_packing: false | |
| pad_to_sequence_len: true | |
| lora_r: 32 | |
| lora_alpha: 16 | |
| lora_dropout: 0.05 | |
| lora_target_modules: | |
| lora_target_linear: true | |
| lora_fan_in_fan_out: | |
| wandb_project: neocortex | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_run_id: | |
| wandb_log_model: checkpoint | |
| gradient_accumulation_steps: 4 | |
| micro_batch_size: 2 | |
| num_epochs: 3 | |
| optimizer: paged_adamw_32bit | |
| lr_scheduler: cosine | |
| learning_rate: 0.0002 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: auto | |
| fp16: | |
| tf32: false | |
| gradient_checkpointing: true | |
| early_stopping_patience: | |
| resume_from_checkpoint: | |
| local_rank: | |
| logging_steps: 1 | |
| xformers_attention: | |
| flash_attention: true | |
| warmup_steps: 10 | |
| eval_steps: 20 | |
| eval_table_size: 5 | |
| save_strategy: epoch | |
| save_steps: | |
| debug: | |
| deepspeed: | |
| weight_decay: 0.0 | |
| fsdp: | |
| fsdp_config: | |
| special_tokens: | |
| bos_token: "<s>" | |
| eos_token: "</s>" | |
| unk_token: "<unk>" | |
| ``` | |
| </details><br> | |
| # neocortex-grounded | |
| This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.1270 | |
| ## 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: 0.0002 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 2 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 16 | |
| - total_eval_batch_size: 4 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 10 | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.7091 | 0.01 | 1 | 1.7034 | | |
| | 1.3312 | 0.29 | 20 | 1.2385 | | |
| | 1.1599 | 0.58 | 40 | 1.1702 | | |
| | 1.1673 | 0.87 | 60 | 1.1425 | | |
| | 1.0802 | 1.16 | 80 | 1.1291 | | |
| | 1.0736 | 1.45 | 100 | 1.1238 | | |
| | 1.0308 | 1.74 | 120 | 1.1185 | | |
| | 1.0042 | 2.03 | 140 | 1.1110 | | |
| | 0.997 | 2.32 | 160 | 1.1274 | | |
| | 0.8535 | 2.61 | 180 | 1.1278 | | |
| | 0.9331 | 2.9 | 200 | 1.1270 | | |
| ### Framework versions | |
| - PEFT 0.9.0 | |
| - Transformers 4.39.0.dev0 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.17.1 | |
| - Tokenizers 0.15.0 |