adapter: qlora
auto_resume_from_checkpoints: true
base_model: lmsys/vicuna-7b-v1.5
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 7bda971689dc7164_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/7bda971689dc7164_train_data.json
  type:
    field_input: body
    field_instruction: title
    field_output: question_content
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 4
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: error577/ee768b8f-228a-4bf6-becc-812a3a534422
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: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: null
micro_batch_size: 2
mlflow_experiment_name: /tmp/7bda971689dc7164_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch_4bit
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: 512
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: e47a6fee-59bc-4914-a838-c16abf8d413e
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e47a6fee-59bc-4914-a838-c16abf8d413e
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null

ee768b8f-228a-4bf6-becc-812a3a534422

This model is a fine-tuned version of lmsys/vicuna-7b-v1.5 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0129

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_4BIT 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
0.3601	0.0004	1	0.4136
0.0558	0.0422	100	0.0175
0.0127	0.0844	200	0.0097
0.0692	0.1265	300	0.0121
0.053	0.1687	400	0.0118
0.0022	0.2109	500	0.0114
0.0193	0.2531	600	0.0129

Framework versions

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