See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 95df966ebd54953c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/95df966ebd54953c_train_data.json
  type:
    field_input: "\uBCF8\uBB38"
    field_instruction: "\uC81C\uBAA9"
    field_output: "\uCE74\uD14C\uACE0\uB9AC"
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: fedovtt/46625f8a-0583-4960-ad89-42556bb00d0c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 75GiB
max_steps: 25
micro_batch_size: 2
mlflow_experiment_name: /tmp/95df966ebd54953c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
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: 5
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 46625f8a-0583-4960-ad89-42556bb00d0c
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 46625f8a-0583-4960-ad89-42556bb00d0c
warmup_steps: 5
weight_decay: 0.1
xformers_attention: true

46625f8a-0583-4960-ad89-42556bb00d0c

This model is a fine-tuned version of WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2080

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
• training_steps: 25

Training results

Training Loss	Epoch	Step	Validation Loss
7.1042	0.0001	1	7.1512
7.5328	0.0003	3	7.0207
6.0895	0.0005	6	4.5371
1.8198	0.0008	9	1.1247
0.3309	0.0010	12	0.4181
0.231	0.0013	15	0.2874
0.0864	0.0015	18	0.2430
0.1357	0.0018	21	0.2168
0.1002	0.0020	24	0.2080

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.5.0+cu124
• Datasets 3.0.1
• Tokenizers 0.20.1