See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: jingyeom/seal3.1.6n_7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 7737165ada5dea7f_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/7737165ada5dea7f_train_data.json
  type:
    field_input: choices
    field_instruction: task
    field_output: answer
    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: 256
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: gavrilstep/02e6a9fb-88e3-4918-abcc-8e75ed1e72a1
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 3
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_memory:
  0: 75GiB
max_steps: 40
micro_batch_size: 2
mlflow_experiment_name: /tmp/7737165ada5dea7f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
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: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4c663774-e928-4e66-a1d5-0f6880301355
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 4c663774-e928-4e66-a1d5-0f6880301355
warmup_steps: 10
weight_decay: 0.01
xformers_attention: true

02e6a9fb-88e3-4918-abcc-8e75ed1e72a1

This model is a fine-tuned version of jingyeom/seal3.1.6n_7b on the None dataset. It achieves the following results on the evaluation set:

• Loss: nan

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: 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: 10
• training_steps: 40

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0111	1	nan
0.0	0.0554	5	nan
0.0	0.1108	10	nan
0.0	0.1662	15	nan
0.0	0.2216	20	nan
0.0	0.2770	25	nan
0.0	0.3324	30	nan
0.0	0.3878	35	nan
0.0	0.4432	40	nan

Framework versions

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