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---
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
results: []
datasets:
- Open-Orca/SlimOrca-Dedup
language:
- en
---
# TinyLlama-1.1B-SlimOrca
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
[<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
hub_model_id: gardner/TinyLlama-1.1B-SlimOrca
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>
## 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 |