---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model-index:
- name: lora_test
  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: `0.4.0`
```yaml
adapter: lora
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
bf16: auto
dataset_prepared_path: null
datasets:
- path: joseagmz/MedQnA_version3
  type: context_qa.load_v2
debug: null
deepspeed: null
early_stopping_patience: null
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
is_llama_derived_model: true
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
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
lr_scheduler: cosine
micro_batch_size: 2
model_type: LlamaForCausalLM
num_epochs: 4
optimizer: adamw_bnb_8bit
output_dir: ./lora_test
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>

# lora_test

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: 0.7337

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6541        | 0.01  | 1    | 1.7634          |
| 1.2512        | 0.25  | 42   | 0.8978          |
| 1.1008        | 0.5   | 84   | 0.8307          |
| 1.0685        | 0.75  | 126  | 0.8026          |
| 1.1573        | 1.0   | 168  | 0.7850          |
| 0.9346        | 1.24  | 210  | 0.7729          |
| 1.0299        | 1.49  | 252  | 0.7612          |
| 1.0057        | 1.74  | 294  | 0.7544          |
| 0.976         | 1.99  | 336  | 0.7478          |
| 1.0765        | 2.22  | 378  | 0.7439          |
| 0.8845        | 2.47  | 420  | 0.7409          |
| 1.0198        | 2.73  | 462  | 0.7379          |
| 0.9712        | 2.98  | 504  | 0.7352          |
| 0.9069        | 3.21  | 546  | 0.7350          |
| 0.8973        | 3.46  | 588  | 0.7342          |
| 0.9359        | 3.71  | 630  | 0.7337          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.17.1
- Tokenizers 0.15.0