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jeiku
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Aura-MoEv2

Text Generation
Transformers
Safetensors
mixtral
axolotl
Generated from Trainer
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
Aura-MoEv2
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---
library_name: transformers
base_model: jeiku/MoEv2
tags:
- axolotl
- generated_from_trainer
datasets:
- FourOhFour/RP_Phase
- jeiku/Writing
model-index:
- name: Aura-MoEv2
  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: `0.6.0`
```yaml
base_model: jeiku/MoEv2
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: FourOhFour/RP_Phase
    type: chat_template
    chat_template: chatml
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: jeiku/Writing
    type: completion
    field: text

chat_template: chatml

shuffle_merged_datasets: true
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./output/out

hub_model_id: jeiku/Aura-MoEv2
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len:

wandb_project: Aura-MoEv2
wandb_entity:
wandb_watch:
wandb_name: Aura-MoEv2
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 2
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00005

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

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 1
debug:
deepspeed: 
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|finetune_right_pad_id|>
```

</details><br>

# Aura-MoEv2

This model is a fine-tuned version of [jeiku/MoEv2](https://huggingface.co/jeiku/MoEv2) on the FourOhFour/RP_Phase and the jeiku/Writing datasets.
It achieves the following results on the evaluation set:
- Loss: 1.7106

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- 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: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 29.5342       | 0.0038 | 1    | 1.8693          |
| 27.8562       | 0.4990 | 130  | 1.7601          |
| 26.632        | 0.9981 | 260  | 1.6990          |
| 21.9675       | 1.4952 | 390  | 1.7117          |
| 21.648        | 1.9942 | 520  | 1.7106          |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.3.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0