adapter: qlora
auto_resume_from_checkpoints: true
base_model: samoline/5255b81c-a162-4f3a-9a96-f4d344cb99c8
bf16: auto
chat_template: llama3
dataset_prepared_path: null
dataset_processes: 6
datasets:
- data_files:
  - 6af671d7f222ce26_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/6af671d7f222ce26_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
deepspeed: null
early_stopping_patience: 5
eval_max_new_tokens: 128
eval_steps: 200
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: error577/47615d62-b7f3-4efd-b567-61a937215b9f
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: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: null
micro_batch_size: 4
mlflow_experiment_name: /tmp/6af671d7f222ce26_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
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: 200
sequence_len: 256
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.005
wandb_entity: null
wandb_mode: online
wandb_name: 5b76a1a8-f96b-44f2-9330-30f496d3f59a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 5b76a1a8-f96b-44f2-9330-30f496d3f59a
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null

47615d62-b7f3-4efd-b567-61a937215b9f

This model is a fine-tuned version of samoline/5255b81c-a162-4f3a-9a96-f4d344cb99c8 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8456

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: 4
total_train_batch_size: 16
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: 30
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.7552	0.0001	1	0.7747
1.0145	0.0219	200	0.8068
1.0147	0.0437	400	0.8067
0.7413	0.0656	600	0.8176
0.8655	0.0875	800	0.8182
0.5897	0.1094	1000	0.8338
0.866	0.1312	1200	0.8312
0.5207	0.1531	1400	0.8338
0.8087	0.1750	1600	0.8419
0.8481	0.1969	1800	0.8370
0.6158	0.2187	2000	0.8424
0.7539	0.2406	2200	0.8447
0.8562	0.2625	2400	0.8456

Framework versions

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