---
license: llama3
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model-index:
- name: test-file-system/axolotl/test-file-system/axolotl/lora-llama3-8b-chat
results: []
---
[
](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config
axolotl version: `0.4.1`
```yaml
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer # PreTrainedTokenizerFast
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: /test-file-system/axolotl/test-file-system/axolotl/ft_data_sharegpt.jsonl
type: sharegpt
conversation: chatml
#field_human: user
#field_model: assistant
#roles:
# input:
# - user
# - system
# output:
# - assistant
dataset_prepared_path:
val_set_size: 0.05
output_dir: /test-file-system/axolotl/test-file-system/axolotl/lora-llama3-8b-chat
adapter: qlora
lora_model_dir:
sequence_len: 4096
sample_packing: false
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: 2
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
s2_attention:
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
# test-file-system/axolotl/test-file-system/axolotl/lora-llama3-8b-chat
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.8618 | 0.0280 | 1 | 1.8569 |
| 0.0185 | 0.2517 | 9 | 0.0596 |
| 0.0056 | 0.5035 | 18 | 0.0202 |
| 0.0008 | 0.7552 | 27 | 0.0005 |
| 0.0006 | 1.0070 | 36 | 0.0002 |
| 0.0001 | 1.2587 | 45 | 0.0000 |
| 0.0004 | 1.5105 | 54 | 0.0004 |
| 0.0007 | 1.7622 | 63 | 0.0002 |
| 0.0001 | 2.0140 | 72 | 0.0001 |
| 0.0001 | 2.2657 | 81 | 0.0002 |
| 0.0006 | 2.5175 | 90 | 0.0004 |
| 0.0006 | 2.7692 | 99 | 0.0004 |
| 0.0005 | 3.0210 | 108 | 0.0003 |
| 0.0003 | 3.2727 | 117 | 0.0002 |
| 0.0004 | 3.5245 | 126 | 0.0002 |
| 0.0006 | 3.7762 | 135 | 0.0002 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1