---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: gardner/TinyLlama-1.1B-Instruct-3T
model-index:
- name: TinyLlama-1.1B-SlimOrca-LoRA
  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
base_model: gardner/TinyLlama-1.1B-Instruct-3T
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: Open-Orca/SlimOrca-Dedup
    type: sharegpt
    split: train

dataset_prepared_path: ./dsprepare/Open-Orca/SlimOrca-Dedup
val_set_size: 0.05
output_dir: ./tinyllama-1.1b-slimorca-lora
hub_model_id: gardner/TinyLlama-1.1B-SlimOrca-LoRA

sequence_len: 4096
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: tinyllama
wandb_entity: gardner
wandb_name: tinyllama-slimorca

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: true
fp16: false
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>

# TinyLlama-1.1B-SlimOrca-LoRA

This model is a fine-tuned version of [gardner/TinyLlama-1.1B-Instruct-3T](https://huggingface.co/gardner/TinyLlama-1.1B-Instruct-3T) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5636

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2902        | 0.0   | 1     | 0.9116          |
| 1.0653        | 0.25  | 1126  | 0.6458          |
| 1.0279        | 0.5   | 2252  | 0.6187          |
| 0.8918        | 0.75  | 3378  | 0.6042          |
| 0.9362        | 1.0   | 4504  | 0.5924          |
| 0.8138        | 1.23  | 5630  | 0.5863          |
| 0.9669        | 1.48  | 6756  | 0.5814          |
| 1.019         | 1.73  | 7882  | 0.5742          |
| 0.9232        | 1.98  | 9008  | 0.5695          |
| 0.8507        | 2.22  | 10134 | 0.5700          |
| 0.7542        | 2.47  | 11260 | 0.5662          |
| 0.8325        | 2.72  | 12386 | 0.5639          |
| 0.7913        | 2.97  | 13512 | 0.5617          |
| 0.8372        | 3.2   | 14638 | 0.5648          |
| 0.8984        | 3.45  | 15764 | 0.5638          |
| 0.7898        | 3.7   | 16890 | 0.5636          |


### Framework versions

- PEFT 0.7.1
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0