See axolotl config

axolotl version: 0.4.1

adapter: lora
auto_find_batch_size: true
base_model: EleutherAI/gpt-neo-125m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - f1bfae7be46056e8_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/f1bfae7be46056e8_train_data.json
  type:
    field_input: intent
    field_instruction: instruction
    field_output: response_8b_instruct
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso18/fdebcb1b-3c51-4fe7-bf2c-3aeb8d04ea39
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 10
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_steps: 500
micro_batch_size: 2
mlflow_experiment_name: /tmp/f1bfae7be46056e8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
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
seed: 180
sequence_len: 512
special_tokens:
  pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
use_cache: false
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: ff228523-b38e-4081-8a47-fba2a3a7734a
wandb_project: 18a
wandb_run: your_name
wandb_runid: ff228523-b38e-4081-8a47-fba2a3a7734a
warmup_steps: 50
weight_decay: 0.01
xformers_attention: true

fdebcb1b-3c51-4fe7-bf2c-3aeb8d04ea39

This model is a fine-tuned version of EleutherAI/gpt-neo-125m on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.7662

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: 5e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 180
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• 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: 50
• training_steps: 500

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0001	1	1.9374
7.6107	0.0051	50	1.8925
7.0242	0.0102	100	1.8307
7.3308	0.0154	150	1.8075
7.0242	0.0205	200	1.7921
6.8691	0.0256	250	1.7818
7.0417	0.0307	300	1.7739
6.9122	0.0358	350	1.7693
7.0597	0.0410	400	1.7677
6.6821	0.0461	450	1.7665
6.912	0.0512	500	1.7662

Framework versions

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