metadata
library_name: peft
license: llama3
base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1e538dae-ff49-499b-90ea-ce55eadbd3da
results: []
See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1
bf16: auto
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- a1bfc420a3c44841_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a1bfc420a3c44841_train_data.json
type:
field_input: other_objects
field_instruction: example_text
field_output: chosen_object
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 1
eval_batch_size: 8
eval_max_new_tokens: 128
eval_steps: 25
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: nttx/1e538dae-ff49-499b-90ea-ce55eadbd3da
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 70GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/a1bfc420a3c44841_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1028
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 50
wandb_entity: null
wandb_mode: online
wandb_name: 1e538dae-ff49-499b-90ea-ce55eadbd3da
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1e538dae-ff49-499b-90ea-ce55eadbd3da
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
1e538dae-ff49-499b-90ea-ce55eadbd3da
This model is a fine-tuned version of tokyotech-llm/Llama-3-Swallow-8B-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.9383 | 0.0015 | 1 | 5.6827 |
0.0 | 0.0377 | 25 | 0.0001 |
0.0 | 0.0755 | 50 | 0.0000 |
0.0 | 0.1132 | 75 | 0.0000 |
0.0 | 0.1509 | 100 | 0.0000 |
0.0 | 0.1887 | 125 | 0.0000 |
0.0 | 0.2264 | 150 | 0.0000 |
0.0 | 0.2642 | 175 | 0.0000 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1