See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: echarlaix/tiny-random-PhiForCausalLM
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5f27bc50ea77f27e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5f27bc50ea77f27e_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
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/ea51a844-1173-4395-b5f2-5ab8cd5edc35
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:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 2520
micro_batch_size: 4
mlflow_experiment_name: /tmp/5f27bc50ea77f27e_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: 21d80ea0-1a50-4729-b510-a987e272f042
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 21d80ea0-1a50-4729-b510-a987e272f042
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
ea51a844-1173-4395-b5f2-5ab8cd5edc35
This model is a fine-tuned version of echarlaix/tiny-random-PhiForCausalLM on the None dataset. It achieves the following results on the evaluation set:
- Loss: 6.6190
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: 1889
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
6.9462 | 0.0011 | 1 | 6.9452 |
6.8211 | 0.1059 | 100 | 6.8012 |
6.7556 | 0.2118 | 200 | 6.7375 |
6.7281 | 0.3178 | 300 | 6.7051 |
6.6707 | 0.4237 | 400 | 6.6791 |
6.6773 | 0.5296 | 500 | 6.6604 |
6.7186 | 0.6355 | 600 | 6.6482 |
6.6813 | 0.7414 | 700 | 6.6399 |
6.6778 | 0.8473 | 800 | 6.6341 |
6.6404 | 0.9533 | 900 | 6.6297 |
6.4898 | 1.0592 | 1000 | 6.6267 |
6.7481 | 1.1651 | 1100 | 6.6241 |
6.5533 | 1.2710 | 1200 | 6.6226 |
7.0404 | 1.3769 | 1300 | 6.6214 |
7.7962 | 1.4829 | 1400 | 6.6202 |
6.7513 | 1.5888 | 1500 | 6.6196 |
6.6431 | 1.6947 | 1600 | 6.6192 |
6.2248 | 1.8006 | 1700 | 6.6191 |
6.1459 | 1.9065 | 1800 | 6.6190 |
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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Base model
echarlaix/tiny-random-PhiForCausalLM