---
base_model: t-bank-ai/T-lite-instruct-0.1
library_name: peft
tags:
- generated_from_trainer
model-index:
- name: t-lite-stalin
  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: `0.4.1`
```yaml
base_model: t-bank-ai/T-lite-instruct-0.1

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: test.jsonl
    type: completion
dataset_prepared_path: prepared_data_tlite
val_set_size: 0.1
output_dir: ./t-lite-stalin

adapter: qlora
lora_model_dir:

sequence_len: 272
sample_packing: true
eval_sample_packing: False
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project: 
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 48
num_epochs: 4
optimizer: adamw_bnb_8bit
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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10
evals_per_epoch: 1
eval_table_size:
eval_max_new_tokens: 1000
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|eot_id|>

```

</details><br>

# t-lite-stalin

This model is a fine-tuned version of [t-bank-ai/T-lite-instruct-0.1](https://huggingface.co./t-bank-ai/T-lite-instruct-0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7248

## 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: 48
- eval_batch_size: 48
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 192
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.4388        | 0.0167 | 1    | 2.2736          |
| 1.7178        | 0.9874 | 59   | 1.7366          |
| 1.5568        | 1.9582 | 118  | 1.7031          |
| 1.4787        | 2.9289 | 177  | 1.7109          |
| 1.4473        | 3.8996 | 236  | 1.7248          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1