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---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model-index:
- name: alpaca-cleaned-tiny-llama
results: []
---
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<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
# - path: mhenrichsen/alpaca_2k_test
- path: yahma/alpaca-cleaned
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/alpaca-cleaned-tiny-llama
hub_model_id: ahmedsamirio/alpaca-cleaned-tiny-llama
sequence_len: 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: alpaca-tiny-llama
wandb_entity: ahmedsamirio
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
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
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# alpaca-cleaned-tiny-llama
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1115
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.3435 | 0.0029 | 1 | 1.4128 |
| 1.1926 | 0.2498 | 85 | 1.1723 |
| 1.1275 | 0.4996 | 170 | 1.1518 |
| 1.1153 | 0.7494 | 255 | 1.1410 |
| 1.1289 | 0.9993 | 340 | 1.1312 |
| 1.1267 | 1.2278 | 425 | 1.1276 |
| 1.1053 | 1.4776 | 510 | 1.1220 |
| 1.1261 | 1.7274 | 595 | 1.1172 |
| 1.0991 | 1.9772 | 680 | 1.1144 |
| 1.0295 | 2.2057 | 765 | 1.1157 |
| 1.086 | 2.4555 | 850 | 1.1131 |
| 1.029 | 2.7054 | 935 | 1.1114 |
| 1.019 | 2.9552 | 1020 | 1.1108 |
| 1.0158 | 3.1830 | 1105 | 1.1113 |
| 1.0297 | 3.4328 | 1190 | 1.1123 |
| 1.0571 | 3.6826 | 1275 | 1.1116 |
| 1.0306 | 3.9324 | 1360 | 1.1115 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1