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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/lora-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: `0.4.0`
```yaml
# # Upload the final model to Huggingface
# hub_model_id: kareemamrr/tinyllama-1.1B_alpaca_2k_lora
# # Store the training logs in weights and biases
# wandb_entity: kamr54
# wandb_project: tinyllama-1.1B_alpaca_2k_lora
# wandb_name: lora-run
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: mhenrichsen/alpaca_2k_test
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out
sequence_len: 4096
sample_packing: true
eval_sample_packing: false
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:
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: auto
fp16:
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>
# outputs/lora-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: 1.2118
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.4615 | 0.08 | 1 | 1.4899 |
| 1.385 | 0.24 | 3 | 1.4883 |
| 1.3675 | 0.48 | 6 | 1.4370 |
| 1.2691 | 0.72 | 9 | 1.3388 |
| 1.2268 | 0.96 | 12 | 1.2973 |
| 1.2526 | 1.16 | 15 | 1.2808 |
| 1.2261 | 1.4 | 18 | 1.2527 |
| 1.135 | 1.6400 | 21 | 1.2343 |
| 1.2694 | 1.88 | 24 | 1.2301 |
| 1.149 | 2.08 | 27 | 1.2242 |
| 1.1515 | 2.32 | 30 | 1.2208 |
| 1.195 | 2.56 | 33 | 1.2196 |
| 1.1129 | 2.8 | 36 | 1.2151 |
| 1.1518 | 3.04 | 39 | 1.2133 |
| 1.1887 | 3.24 | 42 | 1.2115 |
| 1.1002 | 3.48 | 45 | 1.2104 |
| 1.189 | 3.7200 | 48 | 1.2118 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1 |