You can deploy this model and start earning money today!

wangd12/
xbrl_term_llama_3_1_8b_8bits_r8

safetensors

Views

No views yet

See axolotl config

axolotl version: 0.9.1

yaml

base_model: meta-llama/Llama-3.1-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 1
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/xbrl_term_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/xbrl_term_llama_3_1_8b_8bits_r8
peft_use_dora: false
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false
wandb_project: finlora_models
wandb_entity: null
wandb_watch: gradients
wandb_name: xbrl_term_llama_3_1_8b_8bits_r8
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/xbrl_term_llama_3_1_8b_8bits_r8

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the /workspace/FinLoRA/data/train/xbrl_term_train.jsonl dataset. It achieves the following results on the evaluation set:

Loss: 1.5077

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: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 5
gradient_accumulation_steps: 2
total_train_batch_size: 40
total_eval_batch_size: 20
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: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0070	1	2.5692
No log	0.2509	36	1.7055
No log	0.5017	72	1.5480
No log	0.7526	108	1.5077

Framework versions

PEFT 0.15.2
Transformers 4.51.3
Pytorch 2.8.0.dev20250319+cu128
Datasets 3.5.1
Tokenizers 0.21.1

wangd12/xbrl_term_llama_3_1_8b_8bits_r8

workspace/FinLoRA/lora/axolotl-output/xbrl_term_llama_3_1_8b_8bits_r8

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

wangd12/
xbrl_term_llama_3_1_8b_8bits_r8