transformers pytorch safetensors mixtral text-generation generated_from_trainer conversational base_model:N8Programs/llamoe-8x1b license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
base_model: N8Programs/llamoe-8x1b
tags:
- generated_from_trainer
model-index:
- name: out
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: N8Programs/llamoe-8x1b
model_type: MixtralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out

sequence_len: 2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: tinyllamoe
wandb_entity:
wandb_watch:
wandb_name: run-1
wandb_log_model: run-1

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adafactor
lr_scheduler: cosine
learning_rate: 0.0002

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: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# out

This model is a fine-tuned version of [N8Programs/llamoe-8x1b](https://huggingface.co/N8Programs/llamoe-8x1b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7176

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2099        | 0.04  | 1    | 1.2991          |
| 1.3823        | 0.27  | 7    | 1.4997          |
| 10.4722       | 0.54  | 14   | 2.6370          |
| 1.6521        | 0.82  | 21   | 1.4303          |
| 1.6555        | 1.07  | 28   | 1.7053          |
| 1.7864        | 1.34  | 35   | 1.8820          |
| 1.2141        | 1.61  | 42   | 1.6614          |
| 1.1488        | 1.88  | 49   | 1.5619          |
| 0.4733        | 2.14  | 56   | 1.6381          |
| 0.444         | 2.41  | 63   | 1.6311          |
| 0.4717        | 2.68  | 70   | 1.6398          |
| 0.4657        | 2.95  | 77   | 1.5938          |
| 0.1066        | 3.2   | 84   | 1.6952          |
| 0.1547        | 3.48  | 91   | 1.7209          |
| 0.1246        | 3.75  | 98   | 1.7176          |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
