---
license: cc
library_name: peft
tags:
- generated_from_trainer
base_model: Lambent/cosmoem-4x1b
model-index:
- name: lora-out
  results: []
---

Opposite approach from prior version to learning rate and batch size.

Stumbled across some writing on MoE training that suggested to increase learning rate and decrease batch size compared to the dense model.

This version increases the learning rate by x5, and cuts the batch size in half. We did not fear the intermittent single step loss spike.

Validation loss is lower than all so far.

General capabilities comparison:

This model:
|                                      Model                                      |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|---------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[CosMoE-AlpacaLight-v0.6](https://huggingface.co/Lambent/CosMoE-AlpacaLight-v0.6)|   23.3|  52.15|     38.57|   29.01|  35.76|

Highest capability prior version MoE:
|                                      Model                                      |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|---------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[CosMoE-AlpacaLight-v0.3](https://huggingface.co/Lambent/CosMoE-AlpacaLight-v0.3)|  23.44|  51.93|     39.55|   27.99|  35.73|

Dense model, trained:
|                                  Model                                  |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|-------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[CosmoAlpacaLight-1b](https://huggingface.co/Lambent/CosmoAlpacaLight-1b)|  24.28|  51.31|     40.33|   29.47|  36.35|

Original model:
|                          Model                          |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|---------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[cosmo-1b](https://huggingface.co/HuggingFaceTB/cosmo-1b)|  22.97|  52.01|     38.02|   28.73|  35.43|

Overall similar to v0.3. Didn't gain too much in capabilities compared to training the base model.
Better than the base model at every overall capability evaluation, which is a directional improvement from v0.3 at least.
Lower validation loss is also interesting.

[<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: Lambent/cosmoem-4x1b
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca
dataset_prepared_path: prepared-alpaca
val_set_size: 0.05
output_dir: ./lora-out

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

adapter: lora
lora_model_dir:
lora_r: 128
lora_alpha: 16
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: CosMoE-AlpacaLight-v0.61
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.001

lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - w1
  - w2
  - w3

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

loss_watchdog_threshold: 2.0
loss_watchdog_patience: 3

warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.002
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# lora-out

This model is a fine-tuned version of [Lambent/cosmoem-4x1b](https://huggingface.co/Lambent/cosmoem-4x1b) on the alpaca-gpt4 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0477

## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1791        | 0.0   | 1    | 1.3304          |
| 1.0505        | 0.25  | 325  | 1.0720          |
| 0.9862        | 0.5   | 650  | 1.0589          |
| 1.057         | 0.75  | 975  | 1.0477          |


### Framework versions

- PEFT 0.9.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0