adapter: lora
auto_find_batch_size: true
base_model: tiiuae/falcon-rw-1b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - fb041338192f0523_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/fb041338192f0523_train_data.json
  type:
    field_instruction: problem
    field_output: solution
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: true
hub_model_id: tuantmdev/f4c64819-29a4-4678-81ca-705540d8cd74
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 1e-4
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 40
lora_alpha: 64
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_grad_norm: 1.0
max_steps: 400
micro_batch_size: 2
mlflow_experiment_name: /tmp/fb041338192f0523_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
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: 50
save_strategy: steps
sequence_len: 512
special_tokens:
  pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8c6db404-8b21-42e8-bc1b-82fe011326ad
wandb_project: Gradients-On-Demand
wandb_run: unknown
wandb_runid: 8c6db404-8b21-42e8-bc1b-82fe011326ad
warmup_steps: 80
weight_decay: 0.0
xformers_attention: null

f4c64819-29a4-4678-81ca-705540d8cd74

This model is a fine-tuned version of tiiuae/falcon-rw-1b on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2066

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
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: 80
training_steps: 400

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0014	1	1.7522
12.2168	0.0690	50	1.5262
11.6563	0.1381	100	1.4117
10.9867	0.2071	150	1.3243
10.5764	0.2761	200	1.2933
9.8984	0.3451	250	1.2578
10.2069	0.4142	300	1.2326
9.8954	0.4832	350	1.2121
10.3119	0.5522	400	1.2066

Framework versions

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