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README.md

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+
+---
+
+library_name: transformers
+license: gemma
+base_model: jeiku/Dante_9B
+tags:
+- generated_from_trainer
+model-index:
+- name: outputs/out
+  results: []
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/Virgil_9B-GGUF
+This is quantized version of [FourOhFour/Virgil_9B](https://huggingface.co/FourOhFour/Virgil_9B) created using llama.cpp
+
+# Original Model Card
+
+
+<!-- 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: `0.4.1`
+```yaml
+base_model: jeiku/Dante_9B
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: FourOhFour/RP_Phase
+    type: sharegpt
+    conversation: chatml
+
+chat_template: chatml
+
+val_set_size: 0.0025
+output_dir: ./outputs/out
+
+adapter:
+lora_r:
+lora_alpha:
+lora_dropout:
+lora_target_linear:
+
+sequence_len: 8192
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: false
+liger_swiglu: true
+liger_fused_linear_cross_entropy: false
+
+wandb_project: chatml9B
+wandb_entity:
+wandb_watch:
+wandb_name: chatml9B
+wandb_log_model:
+
+gradient_accumulation_steps: 32
+micro_batch_size: 1
+num_epochs: 2
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.000008
+weight_decay: 0.05
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 2
+
+debug:
+deepspeed: deepspeed_configs/zero3_bf16.json
+fsdp:
+fsdp_config:
+
+special_tokens:
+  pad_token: <pad>
+
+```
+
+</details><br>
+
+# outputs/out
+
+This model is a fine-tuned version of [jeiku/Dante_9B](https://huggingface.co/jeiku/Dante_9B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7075
+
+## 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: 8e-06
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 128
+- 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: 14
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 1.7474        | 0.0135 | 1    | 1.7996          |
+| 1.6968        | 0.2570 | 19   | 0.9551          |
+| 1.6583        | 0.5139 | 38   | 0.8805          |
+| 1.5418        | 0.7709 | 57   | 0.7926          |
+| 1.3997        | 1.0271 | 76   | 0.7500          |
+| 1.3921        | 1.2847 | 95   | 0.7168          |
+| 1.4141        | 1.5424 | 114  | 0.7155          |
+| 1.4139        | 1.8    | 133  | 0.7075          |
+
+
+### Framework versions
+
+- Transformers 4.46.0.dev0
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.20.0
+