peft tensorboard safetensors llama generated_from_trainer base_model:NousResearch/Llama-2-7b-chat-hf 4-bit bitsandbytes region:us
---
library_name: peft
tags:
- generated_from_trainer
base_model: NousResearch/Llama-2-7b-chat-hf
model-index:
- name: qlora-out_7b_chat_llama
  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: NousResearch/Llama-2-7b-hf
base_model: NousResearch/Llama-2-7b-chat-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: /home/ubuntu/Project_Files/Finetune/Data/json_files/combined_sentences.json
    type: completion
    ds_type: json
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./qlora-out_7b_chat_llama

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
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:

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 4
optimizer: paged_adamw_32bit
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: 10
eval_table_size:
saves_per_epoch: 2
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# qlora-out_7b_chat_llama

This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5727

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 4.1915        | 0.0   | 1    | 4.1955          |
| 0.6659        | 0.1   | 95   | 0.5660          |
| 0.6226        | 0.2   | 190  | 0.5444          |
| 0.6062        | 0.3   | 285  | 0.5330          |
| 0.594         | 0.4   | 380  | 0.5310          |
| 0.5866        | 0.5   | 475  | 0.5322          |
| 0.5801        | 0.6   | 570  | 0.5280          |
| 0.5772        | 0.7   | 665  | 0.5270          |
| 0.5741        | 0.8   | 760  | 0.5285          |
| 0.5719        | 0.9   | 855  | 0.5279          |
| 0.5674        | 1.0   | 950  | 0.5313          |
| 0.5673        | 1.09  | 1045 | 0.5360          |
| 0.5652        | 1.19  | 1140 | 0.5323          |
| 0.5609        | 1.29  | 1235 | 0.5292          |
| 0.5615        | 1.39  | 1330 | 0.5329          |
| 0.5589        | 1.49  | 1425 | 0.5346          |
| 0.5572        | 1.59  | 1520 | 0.5364          |
| 0.5567        | 1.69  | 1615 | 0.5392          |
| 0.557         | 1.8   | 1710 | 0.5396          |
| 0.5549        | 1.9   | 1805 | 0.5454          |
| 0.5517        | 2.0   | 1900 | 0.5475          |
| 0.5493        | 2.08  | 1995 | 0.5515          |
| 0.5506        | 2.18  | 2090 | 0.5544          |
| 0.5497        | 2.29  | 2185 | 0.5507          |
| 0.548         | 2.39  | 2280 | 0.5563          |
| 0.5483        | 2.49  | 2375 | 0.5578          |
| 0.5502        | 2.59  | 2470 | 0.5602          |
| 0.5472        | 2.69  | 2565 | 0.5632          |
| 0.548         | 2.79  | 2660 | 0.5649          |
| 0.5478        | 2.89  | 2755 | 0.5630          |
| 0.5441        | 2.99  | 2850 | 0.5663          |
| 0.5418        | 3.08  | 2945 | 0.5705          |
| 0.5453        | 3.18  | 3040 | 0.5679          |
| 0.5431        | 3.28  | 3135 | 0.5717          |
| 0.5451        | 3.38  | 3230 | 0.5734          |
| 0.5469        | 3.48  | 3325 | 0.5711          |
| 0.5445        | 3.58  | 3420 | 0.5734          |
| 0.5428        | 3.68  | 3515 | 0.5724          |
| 0.5436        | 3.78  | 3610 | 0.5730          |
| 0.546         | 3.88  | 3705 | 0.5727          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0