Instructions to use ronniealfaro/mythos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ronniealfaro/mythos with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "ronniealfaro/mythos") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ronniealfaro/mythos: direct link, hf CLI and curl.
- Browser
- Download file 2.98 kB
-
https://huggingface.co/ronniealfaro/mythos/resolve/main/README.md
- Command line
-
hf download hf://ronniealfaro/mythos/README.md
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curl -L -o README.md https://huggingface.co/ronniealfaro/mythos/resolve/main/README.md
2.98 kB
| library_name: peft | |
| license: apache-2.0 | |
| base_model: NousResearch/Hermes-2-Pro-Mistral-7B | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - ronniealfaro/mythos | |
| model-index: | |
| - name: outputs/mymodel | |
| 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: `0.8.0.dev0` | |
| ```yaml | |
| adapter: lora | |
| base_model: NousResearch/Hermes-2-Pro-Mistral-7B | |
| bf16: auto | |
| dataset_processes: 32 | |
| datasets: | |
| - path: ronniealfaro/mythos | |
| type: completion | |
| field: response | |
| prompt_field: prompt | |
| system_field: context | |
| trust_remote_code: false | |
| gradient_accumulation_steps: 1 | |
| gradient_checkpointing: false | |
| learning_rate: 0.0002 | |
| lisa_layers_attribute: model.layers | |
| load_best_model_at_end: false | |
| load_in_4bit: true | |
| load_in_8bit: false | |
| lora_alpha: 16 | |
| lora_dropout: 0.05 | |
| lora_r: 8 | |
| lora_target_modules: | |
| - q_proj | |
| - v_proj | |
| - k_proj | |
| - o_proj | |
| - gate_proj | |
| - down_proj | |
| - up_proj | |
| loraplus_lr_embedding: 1.0e-06 | |
| lr_scheduler: cosine | |
| max_prompt_len: 512 | |
| mean_resizing_embeddings: false | |
| micro_batch_size: 16 | |
| num_epochs: 1.0 | |
| optimizer: paged_adamw_32bit | |
| output_dir: ./outputs/mymodel | |
| pretrain_multipack_attn: true | |
| pretrain_multipack_buffer_size: 10000 | |
| qlora_sharded_model_loading: false | |
| ray_num_workers: 1 | |
| resources_per_worker: | |
| GPU: 1 | |
| sample_packing_bin_size: 200 | |
| sample_packing_group_size: 100000 | |
| save_only_model: false | |
| save_safetensors: true | |
| sequence_len: 4096 | |
| shuffle_merged_datasets: true | |
| skip_prepare_dataset: false | |
| strict: false | |
| train_on_inputs: false | |
| trl: | |
| log_completions: false | |
| ref_model_mixup_alpha: 0.9 | |
| ref_model_sync_steps: 64 | |
| sync_ref_model: false | |
| use_vllm: false | |
| vllm_device: auto | |
| vllm_dtype: auto | |
| vllm_gpu_memory_utilization: 0.9 | |
| use_ray: false | |
| val_set_size: 0.0 | |
| weight_decay: 0.0 | |
| ``` | |
| </details><br> | |
| # outputs/mymodel | |
| This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) on the ronniealfaro/mythos 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: 0.0002 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: cosine | |
| - num_epochs: 1.0 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.14.0 | |
| - Transformers 4.49.0 | |
| - Pytorch 2.5.1+cu124 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 |