See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: EleutherAI/gpt-neo-125m
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- aa838734bbefd9c8_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/aa838734bbefd9c8_train_data.json
type:
field_instruction: prompt
field_output: GEITje-7B-ultra
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
device_map:
? ''
: 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: false
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/4ce2ceca-17a8-49c5-83c2-35618600ca7f
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
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
lora_target_modules: null
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 4140
micro_batch_size: 4
mlflow_experiment_name: /tmp/aa838734bbefd9c8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: 100
sequence_len: 1024
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: 0130bc26-f4a9-49f9-b382-a5b971eeaf04
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0130bc26-f4a9-49f9-b382-a5b971eeaf04
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
4ce2ceca-17a8-49c5-83c2-35618600ca7f
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: 2.2814
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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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
- training_steps: 3002
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
22.3516 | 0.0007 | 1 | 2.7369 |
20.2093 | 0.0666 | 100 | 2.5747 |
19.7446 | 0.1333 | 200 | 2.5223 |
20.6209 | 0.1999 | 300 | 2.4892 |
20.299 | 0.2666 | 400 | 2.4635 |
19.2732 | 0.3332 | 500 | 2.4406 |
19.7515 | 0.3998 | 600 | 2.4214 |
18.9969 | 0.4665 | 700 | 2.4035 |
19.396 | 0.5331 | 800 | 2.3888 |
18.8607 | 0.5998 | 900 | 2.3739 |
18.908 | 0.6664 | 1000 | 2.3621 |
18.5906 | 0.7330 | 1100 | 2.3504 |
18.9578 | 0.7997 | 1200 | 2.3409 |
19.0391 | 0.8663 | 1300 | 2.3319 |
18.6685 | 0.9329 | 1400 | 2.3235 |
15.9064 | 0.9996 | 1500 | 2.3170 |
16.0333 | 1.0664 | 1600 | 2.3104 |
17.8283 | 1.1330 | 1700 | 2.3052 |
18.3232 | 1.1997 | 1800 | 2.3006 |
16.2907 | 1.2663 | 1900 | 2.2966 |
20.2129 | 1.3329 | 2000 | 2.2936 |
17.9803 | 1.3996 | 2100 | 2.2905 |
17.2894 | 1.4662 | 2200 | 2.2879 |
18.2833 | 1.5329 | 2300 | 2.2861 |
16.9763 | 1.5995 | 2400 | 2.2844 |
18.0936 | 1.6661 | 2500 | 2.2834 |
17.6034 | 1.7328 | 2600 | 2.2823 |
17.8223 | 1.7994 | 2700 | 2.2819 |
18.5859 | 1.8661 | 2800 | 2.2816 |
17.2502 | 1.9327 | 2900 | 2.2815 |
17.3237 | 1.9993 | 3000 | 2.2814 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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Model tree for Alphatao/4ce2ceca-17a8-49c5-83c2-35618600ca7f
Base model
EleutherAI/gpt-neo-125m