---
base_model: Trisert/tinyllama-alpaca
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: outputs/qlora-out-context
  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: Trisert/tinyllama-alpaca
bf16: false
dataset_prepared_path: null
datasets:
- ds_tipe: json
  path: /content/pubmed_continual_pretraning_dataset.jsonl
  type: completion
debug: null
deepspeed: null
early_stopping_patience: null
eval_sample_packing: false
evals_per_epoch: 4
flash_attention: false
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: 8
model_type: AutoModelForCausalLM
num_epochs: 4
optimizer: paged_adamw_32bit
output_dir: ./outputs/qlora-out-context
pad_to_sequence_len: false
resume_from_checkpoint: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 4096
special_tokens: null
strict: false
tf32: false
tokenizer_type: AutoTokenizer
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-context

This model is a fine-tuned version of [Trisert/tinyllama-alpaca](https://huggingface.co/Trisert/tinyllama-alpaca) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8030

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.6905        | 0.0336 | 1    | 2.7292          |
| 2.4725        | 0.2689 | 8    | 2.3972          |
| 1.9891        | 0.5378 | 16   | 2.0718          |
| 1.8345        | 0.8067 | 24   | 1.9329          |
| 1.8088        | 1.0756 | 32   | 1.8730          |
| 1.8183        | 1.3445 | 40   | 1.8430          |
| 1.8004        | 1.6134 | 48   | 1.8263          |
| 1.7674        | 1.8824 | 56   | 1.8167          |
| 1.7164        | 2.1513 | 64   | 1.8104          |
| 1.6525        | 2.4202 | 72   | 1.8069          |
| 1.7917        | 2.6891 | 80   | 1.8053          |
| 1.8022        | 2.9580 | 88   | 1.8037          |
| 1.6917        | 3.2269 | 96   | 1.8032          |
| 1.765         | 3.4958 | 104  | 1.8030          |
| 1.6784        | 3.7647 | 112  | 1.8030          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1