---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: openlm-research/open_llama_3b_v2
model-index:
- name: qlora-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
base_model: openlm-research/open_llama_3b_v2
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
push_dataset_to_hub:
datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
adapter: qlora
lora_model_dir:
sequence_len: 1024
sample_packing: true
lora_r: 8
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./qlora-out
gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
torchdistx_path:
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: false
fp16: true
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
gptq_groupsize:
gptq_model_v1:
warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

```

</details><br>

# qlora-out

This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4177

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3026        | 0.01  | 1    | 1.3435          |
| 1.1146        | 0.25  | 50   | 1.1476          |
| 1.2387        | 0.5   | 100  | 1.1319          |
| 1.4159        | 0.75  | 150  | 1.1192          |
| 1.2807        | 1.01  | 200  | 1.1153          |
| 1.0465        | 1.24  | 250  | 1.1569          |
| 0.9577        | 1.49  | 300  | 1.1493          |
| 1.1257        | 1.74  | 350  | 1.1462          |
| 0.9404        | 1.99  | 400  | 1.1520          |
| 0.7161        | 2.22  | 450  | 1.2603          |
| 0.5897        | 2.47  | 500  | 1.2661          |
| 0.5271        | 2.72  | 550  | 1.2814          |
| 0.6239        | 2.97  | 600  | 1.2705          |
| 0.3486        | 3.21  | 650  | 1.3848          |
| 0.5591        | 3.46  | 700  | 1.4171          |
| 0.3804        | 3.71  | 750  | 1.4177          |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0