Instructions to use Jboadu/test-model-1-pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jboadu/test-model-1-pretrain with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jboadu/test-model-1-pretrain", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: mistralai/Mistral-7B-Instruct-v0.3 | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| datasets: | |
| - representation_variation_GAIA_Raw_Training_Data.jsonl | |
| - text_chunks_GAIA_Raw_Training_Data.jsonl | |
| - inferred_facts_GAIA_Raw_Training_Data.jsonl | |
| model-index: | |
| - name: test-model-1-pretrain | |
| 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 | |
| base_model: mistralai/Mistral-7B-Instruct-v0.3 | |
| tokenizer_type: AutoTokenizer | |
| model_type: AutoModelForCausalLM | |
| load_in_8bit: false | |
| load_in_4bit: false | |
| strict: false | |
| datasets: | |
| - path: representation_variation_GAIA_Raw_Training_Data.jsonl | |
| type: completion | |
| - path: text_chunks_GAIA_Raw_Training_Data.jsonl | |
| type: completion | |
| - path: inferred_facts_GAIA_Raw_Training_Data.jsonl | |
| type: completion | |
| dataset_prepared_path: last_run_prepared | |
| output_dir: ./model-output | |
| seed: 1337 | |
| sequence_len: 5000 | |
| sample_packing: true | |
| pad_to_sequence_len: false | |
| shuffle_merged_datasets: true | |
| gradient_accumulation_steps: 75 | |
| micro_batch_size: 2 | |
| eval_batch_size: 4 | |
| num_epochs: 7 | |
| optimizer: paged_adamw_8bit | |
| lr_scheduler: constant | |
| learning_rate: 2.0e-05 | |
| noisy_embedding_alpha: 5 | |
| weight_decay: 0 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: true | |
| fp16: false | |
| tf32: false | |
| gradient_checkpointing: true | |
| logging_steps: 1 | |
| xformers_attention: false | |
| flash_attention: false | |
| chat_template: chatml | |
| auto_resume_from_checkpoints: false | |
| warmup_ratio: 0.1 | |
| evals_per_epoch: 1 | |
| val_set_size: 0.04 | |
| saves_per_epoch: 1 | |
| eval_sample_packing: false | |
| save_total_limit: 2 | |
| special_tokens: | |
| pad_token: <unk> | |
| use_liger_kernel: true | |
| plugins: | |
| - axolotl.integrations.liger.LigerPlugin | |
| liger_rope: true | |
| liger_rms_norm: true | |
| liger_glu_activation: true | |
| liger_layer_norm: true | |
| liger_fused_linear_cross_entropy: true | |
| sequence_length: 10000 | |
| wandb_project: test-project | |
| wandb_entity: "" | |
| wandb_watch: "" | |
| wandb_run_id: "" | |
| wandb_log_model: "" | |
| hub_model_id: Jboadu/test-model-1-pretrain | |
| hub_strategy: all_checkpoints | |
| ``` | |
| </details><br> | |
| # test-model-1-pretrain | |
| This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the representation_variation_GAIA_Raw_Training_Data.jsonl, the text_chunks_GAIA_Raw_Training_Data.jsonl and the inferred_facts_GAIA_Raw_Training_Data.jsonl datasets. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.2857 | |
| ## 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: 2e-05 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 4 | |
| - seed: 1337 | |
| - gradient_accumulation_steps: 75 | |
| - total_train_batch_size: 150 | |
| - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: constant | |
| - num_epochs: 7.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 2.7564 | 0.7426 | 1 | 2.1477 | | |
| | 4.0475 | 1.7426 | 2 | 2.0678 | | |
| | 3.5711 | 2.7426 | 3 | 2.3400 | | |
| | 3.3781 | 3.7426 | 4 | 1.9086 | | |
| | 3.2075 | 4.7426 | 5 | 1.6236 | | |
| | 2.3991 | 5.7426 | 6 | 1.4519 | | |
| | 2.1077 | 6.7426 | 7 | 1.2857 | | |
| ### Framework versions | |
| - Transformers 4.49.0 | |
| - Pytorch 2.5.1+cu124 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 | |