See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: microsoft/phi-1_5
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 77552880fa7295ff_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/77552880fa7295ff_train_data.json
  type:
    field_input: CUDA_Code
    field_instruction: Op_Name
    field_output: PyTorch_Code_Functional
    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/d6e286f9-a15d-43e9-a540-cc79dd524779
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: 4140
micro_batch_size: 4
mlflow_experiment_name: /tmp/77552880fa7295ff_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: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: f89db93a-91b1-4e90-816a-7353d8fe5b26
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f89db93a-91b1-4e90-816a-7353d8fe5b26
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

d6e286f9-a15d-43e9-a540-cc79dd524779

This model is a fine-tuned version of microsoft/phi-1_5 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0009

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: 1753

Training results

Training Loss	Epoch	Step	Validation Loss
0.5151	0.0011	1	0.5460
0.0341	0.1141	100	0.0318
0.0045	0.2282	200	0.0048
0.0016	0.3424	300	0.0018
0.0009	0.4565	400	0.0007
0.0004	0.5706	500	0.0004
0.0003	0.6847	600	0.0003
0.0002	0.7989	700	0.0002
0.0006	0.9130	800	0.0003
0.0011	1.0271	900	0.0009

Framework versions

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