[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.9.1.post1`
```yaml
base_model: meta-llama/Llama-3.1-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
gradient_accumulation_steps: 2
micro_batch_size: 8
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0001
load_in_8bit: true
load_in_4bit: false
adapter: lora
lora_model_dir: null
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
- k_proj
datasets:
- path: /workspace/FinLoRA/data/train/headline_train.jsonl
type:
system_prompt: ''
field_system: system
field_instruction: context
field_output: target
format: '[INST] {instruction} [/INST]'
no_input_format: '[INST] {instruction} [/INST]'
dataset_prepared_path: null
val_set_size: 0.02
output_dir: /workspace/FinLoRA/lora/axolotl-output/headline_llama_3_1_8b_8bits_r8_rslora
peft_use_dora: false
peft_use_rslora: true
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false
wandb_project: finlora_models
wandb_entity: null
wandb_watch: gradients
wandb_name: headline_llama_3_1_8b_8bits_r8_rslora
wandb_log_model: 'false'
bf16: auto
tf32: false
gradient_checkpointing: true
resume_from_checkpoint: null
logging_steps: 500
flash_attention: false
deepspeed: deepspeed_configs/zero1.json
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
chat_template: llama3
```
# workspace/FinLoRA/lora/axolotl-output/headline_llama_3_1_8b_8bits_r8_rslora
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the /workspace/FinLoRA/data/train/headline_train.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0577
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- total_eval_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0006 | 1 | 7.2775 |
| No log | 0.2504 | 420 | 0.0538 |
| 0.1779 | 0.5007 | 840 | 0.0480 |
| 0.0475 | 0.7511 | 1260 | 0.0431 |
| 0.0404 | 1.0012 | 1680 | 0.0423 |
| 0.0344 | 1.2516 | 2100 | 0.0431 |
| 0.0328 | 1.5019 | 2520 | 0.0505 |
| 0.0328 | 1.7523 | 2940 | 0.0472 |
| 0.0301 | 2.0024 | 3360 | 0.0415 |
| 0.0282 | 2.2528 | 3780 | 0.0551 |
| 0.0222 | 2.5031 | 4200 | 0.0517 |
| 0.0216 | 2.7535 | 4620 | 0.0478 |
| 0.0201 | 3.0036 | 5040 | 0.0500 |
| 0.0201 | 3.2539 | 5460 | 0.0606 |
| 0.0135 | 3.5043 | 5880 | 0.0593 |
| 0.0117 | 3.7547 | 6300 | 0.0577 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0.dev20250319+cu128
- Datasets 3.5.1
- Tokenizers 0.21.1