TinyLLama1.1B_PLM / README.md
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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model-index:
- name: outputs/qlora-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: `0.4.1`
```yaml
adapter: qlora
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
bf16: auto
dataset_prepared_path: null
datasets:
- path: Taiel26/plm_2500_uniref
type: alpaca
debug: null
deepspeed: null
early_stopping_patience: null
eval_sample_packing: false
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
micro_batch_size: 2
model_type: LlamaForCausalLM
num_epochs: 4
optimizer: paged_adamw_32bit
output_dir: ./outputs/qlora-out
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: 1
sequence_len: 4096
special_tokens: null
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# outputs/qlora-out
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: 0.8586
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.0919 | 0.0198 | 1 | 2.0800 |
| 1.5479 | 0.2574 | 13 | 1.5341 |
| 1.2083 | 0.5149 | 26 | 1.2245 |
| 1.0851 | 0.7723 | 39 | 1.0607 |
| 0.9432 | 1.0297 | 52 | 0.9755 |
| 0.9007 | 1.2178 | 65 | 0.9334 |
| 0.8765 | 1.4752 | 78 | 0.9084 |
| 0.8789 | 1.7327 | 91 | 0.8891 |
| 0.8304 | 1.9901 | 104 | 0.8779 |
| 0.8194 | 2.1782 | 117 | 0.8714 |
| 0.848 | 2.4356 | 130 | 0.8665 |
| 0.8354 | 2.6931 | 143 | 0.8627 |
| 0.8476 | 2.9505 | 156 | 0.8605 |
| 0.811 | 3.1386 | 169 | 0.8590 |
| 0.8178 | 3.3960 | 182 | 0.8588 |
| 0.8073 | 3.6535 | 195 | 0.8586 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1