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--- |
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library_name: transformers |
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license: gemma |
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base_model: jeiku/Dante_9B |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: outputs/out |
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results: [] |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/Virgil_9B-GGUF |
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This is quantized version of [FourOhFour/Virgil_9B](https://huggingface.co/FourOhFour/Virgil_9B) created using llama.cpp |
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# Original Model Card |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<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) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.1` |
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```yaml |
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base_model: jeiku/Dante_9B |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: FourOhFour/RP_Phase |
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type: sharegpt |
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conversation: chatml |
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chat_template: chatml |
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val_set_size: 0.0025 |
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output_dir: ./outputs/out |
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adapter: |
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lora_r: |
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lora_alpha: |
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lora_dropout: |
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lora_target_linear: |
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sequence_len: 8192 |
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sample_packing: true |
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eval_sample_packing: false |
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pad_to_sequence_len: true |
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plugins: |
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- axolotl.integrations.liger.LigerPlugin |
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liger_rope: true |
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liger_rms_norm: false |
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liger_swiglu: true |
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liger_fused_linear_cross_entropy: false |
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wandb_project: chatml9B |
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wandb_entity: |
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wandb_watch: |
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wandb_name: chatml9B |
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wandb_log_model: |
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gradient_accumulation_steps: 32 |
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micro_batch_size: 1 |
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num_epochs: 2 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.000008 |
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weight_decay: 0.05 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: true |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_ratio: 0.1 |
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evals_per_epoch: 4 |
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eval_table_size: |
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eval_max_new_tokens: 128 |
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saves_per_epoch: 2 |
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debug: |
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deepspeed: deepspeed_configs/zero3_bf16.json |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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pad_token: <pad> |
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``` |
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</details><br> |
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# outputs/out |
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This model is a fine-tuned version of [jeiku/Dante_9B](https://huggingface.co/jeiku/Dante_9B) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7075 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-06 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 14 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.7474 | 0.0135 | 1 | 1.7996 | |
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| 1.6968 | 0.2570 | 19 | 0.9551 | |
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| 1.6583 | 0.5139 | 38 | 0.8805 | |
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| 1.5418 | 0.7709 | 57 | 0.7926 | |
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| 1.3997 | 1.0271 | 76 | 0.7500 | |
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| 1.3921 | 1.2847 | 95 | 0.7168 | |
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| 1.4141 | 1.5424 | 114 | 0.7155 | |
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| 1.4139 | 1.8 | 133 | 0.7075 | |
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### Framework versions |
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- Transformers 4.46.0.dev0 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.0 |
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