Instructions to use Ritual-Net/answer-emojis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ritual-Net/answer-emojis 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, "Ritual-Net/answer-emojis") - Notebooks
- Google Colab
- Kaggle
| base_model: NousResearch/Llama-2-7b-hf | |
| library_name: peft | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| model-index: | |
| - name: answer-emojis | |
| 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: `0.4.1` | |
| ```yaml | |
| base_model: NousResearch/Llama-2-7b-hf | |
| model_type: LlamaForCausalLM | |
| tokenizer_type: LlamaTokenizer | |
| load_in_8bit: true | |
| load_in_4bit: false | |
| strict: false | |
| datasets: | |
| - path: formatted_math_ratio_02_emojianswers_10k.jsonl | |
| ds_type: json | |
| type: alpaca | |
| val_set_size: 0.05 | |
| dataset_prepared_path: | |
| output_dir: ./outputs/ppml-formatted | |
| hf_use_auth_token: True | |
| hub_model_id: Ritual-Net/answer-emojis | |
| hub_strategy: all_checkpoints | |
| eval_sample_packing: False | |
| sequence_len: 4096 | |
| sample_packing: true | |
| pad_to_sequence_len: true | |
| adapter: lora | |
| lora_model_dir: | |
| lora_r: 32 | |
| lora_alpha: 16 | |
| lora_dropout: 0.05 | |
| lora_target_linear: true | |
| lora_fan_in_fan_out: | |
| wandb_project: ppml | |
| wandb_entity: ritualnah | |
| wandb_watch: | |
| wandb_name: emojianswers | |
| wandb_log_model: "checkpoint" | |
| lora_modules_to_save: | |
| - embed_tokens | |
| - lm_head | |
| gradient_accumulation_steps: 4 | |
| micro_batch_size: 2 | |
| num_epochs: 3 | |
| optimizer: adamw_bnb_8bit | |
| 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 | |
| s2_attention: | |
| warmup_steps: 10 | |
| evals_per_epoch: 2 | |
| eval_table_size: | |
| eval_max_new_tokens: 128 | |
| saves_every_epoch: 1 | |
| debug: | |
| deepspeed: | |
| weight_decay: 0.0 | |
| fsdp: | |
| fsdp_config: | |
| special_tokens: | |
| special_tokens: | |
| bos_token: "<s>" | |
| eos_token: "</s>" | |
| unk_token: "<unk>" | |
| tokens: # these are delimiters | |
| - "[INST]" | |
| - "[/INST]" | |
| ``` | |
| </details><br> | |
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ritualnah/ppml/runs/3q9smy0v) | |
| # answer-emojis | |
| 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: 0.5239 | |
| ## 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 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 8 | |
| - 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 | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 1.0155 | 0.0082 | 1 | 1.2302 | | |
| | 0.5161 | 0.5031 | 61 | 0.5744 | | |
| | 0.5398 | 1.0062 | 122 | 0.5379 | | |
| | 0.4614 | 1.4990 | 183 | 0.5295 | | |
| | 0.4323 | 2.0021 | 244 | 0.5178 | | |
| | 0.3823 | 2.4948 | 305 | 0.5239 | | |
| ### Framework versions | |
| - PEFT 0.11.1 | |
| - Transformers 4.42.3 | |
| - Pytorch 2.1.2+cu118 | |
| - Datasets 2.19.1 | |
| - Tokenizers 0.19.1 |