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

Intuitively it seemed like LISA training should suit a MoE pretty well; though I don't know how well calibrated my intuitions are.

Interesting thing about this one is it looks like it wasn't converging at the end of one epoch. Still more to learn.

Nous capabilities:

|                                    Model                                    |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|-----------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[CosMoEAlpacaLisa-4x1b](https://huggingface.co/Lambent/CosMoEAlpacaLisa-4x1b)|  23.44|  48.13|     41.13|   29.95|  35.66|

Comparisons:

|                                      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|

|                                     Model                                     |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|-------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[CosmoAlpacaLisa-0.3-1b](https://huggingface.co/Lambent/CosmoAlpacaLisa-0.3-1b)|  23.79|  51.61|     40.25|   29.97|  36.41|

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

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


[<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: ./lisa-out

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

lisa_n_layers: 4
lisa_step_interval: 10
lisa_layers_attribute: model.layers

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: CosMoE-AlpacaLisa
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0005

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: 3.0
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>

# lisa-out

This model is a fine-tuned version of [Lambent/cosmoem-4x1b](https://huggingface.co/Lambent/cosmoem-4x1b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2588

## 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: 0.0005
- train_batch_size: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.197         | 0.0   | 1    | 1.5990          |
| 1.4959        | 0.25  | 1383 | 1.4359          |
| 1.6549        | 0.5   | 2766 | 1.3353          |
| 1.3571        | 0.75  | 4149 | 1.2588          |


### Framework versions

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