adapter: qlora
base_model: samoline/b2bb0f6e-174c-481f-a89e-85d6878ea54a
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - train_c4393383-ef1d-4e9c-b95c-18b4f735570d.json
  ds_type: json
  format: custom
  path: /workspace/input_data/train_c4393383-ef1d-4e9c-b95c-18b4f735570d.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
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: error577/4d371d85-3672-4699-9efd-d4d946d1c3bc
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 1
mlflow_experiment_name: /tmp/train_c4393383-ef1d-4e9c-b95c-18b4f735570d.json
model_type: AutoModelForCausalLM
num_epochs: 4
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.02
wandb_entity: null
wandb_mode: online
wandb_name: dd90c6a8-fdf7-491a-8abf-c10f13d6314a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: dd90c6a8-fdf7-491a-8abf-c10f13d6314a
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

4d371d85-3672-4699-9efd-d4d946d1c3bc

This model is a fine-tuned version of samoline/b2bb0f6e-174c-481f-a89e-85d6878ea54a on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2383

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: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
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: 10
training_steps: 100

Training results

Training Loss	Epoch	Step	Validation Loss
1.612	0.0002	1	1.8947
1.0525	0.0049	25	1.4372
1.4032	0.0099	50	1.2714
1.3721	0.0148	75	1.2452
1.0689	0.0197	100	1.2383

Framework versions

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