See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: EleutherAI/pythia-160m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - c7227ec4853193d0_train_data.json
  ds_type: json
  field: prompt
  path: /workspace/input_data/
  split: train
  type: completion
ddp_find_unused_parameters: false
debug: null
deepspeed: null
early_stopping_patience: null
ema_decay: 0.995
ema_update_after_step: 200
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.5
greater_is_better: false
group_by_length: false
hub_model_id: CheapsetZero/c8240d1f-846f-4151-bd2b-8935ac1e50d5
learning_rate: 0.0001
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_nan_inf_filter: true
logging_steps: 1
lora_alpha: 256
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lr_scheduler: cosine
max_steps: 28560
metric_for_best_model: eval_loss
micro_batch_size: 8
min_lr: 1.0e-05
mlflow_experiment_name: /tmp/c7227ec4853193d0_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
reward_model_sampling_temperature: 0.7
s2_attention: null
sample_packing: false
save_total_limit: 3
saves_per_epoch: 4
sequence_len: 1024
special_tokens:
  pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trl:
  beta: 0.15
  max_completion_length: 1024
  num_generations: 16
  reward_funcs:
  - rewards_e62e81b4-5c89-48ea-9288-01c064fb8710.reward_specific_word_count_normalized
  - rewards_e62e81b4-5c89-48ea-9288-01c064fb8710.reward_flesch_kincaid_grade
  - rewards_e62e81b4-5c89-48ea-9288-01c064fb8710.reward_long_words
  - rewards_e62e81b4-5c89-48ea-9288-01c064fb8710.reward_low_syllables_per_word_normalized
  - rewards_e62e81b4-5c89-48ea-9288-01c064fb8710.reward_low_readability
  reward_weights:
  - 5.0
  - 4.501909114541968
  - 5.342897361699535
  - 5.0
  - 4.676309993075206
  use_vllm: false
trust_remote_code: true
use_ema: true
use_peft: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: e62e81b4-5c89-48ea-9288-01c064fb8710
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e62e81b4-5c89-48ea-9288-01c064fb8710
warmup_steps: 2856
weight_decay: 0.01
xformers_attention: null

c8240d1f-846f-4151-bd2b-8935ac1e50d5

This model is a fine-tuned version of EleutherAI/pythia-160m on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.1796

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• 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: 2856
• training_steps: 9730

Training results

Training Loss	Epoch	Step	Validation Loss
14.4886	0.0003	1	6.9197
3.3482	0.2500	811	1.6444
4.248	0.5001	1622	1.9848
4.008	0.7501	2433	1.6788
4.4315	1.0002	3244	1.7052
3.8422	1.2502	4055	1.4894
3.753	1.5002	4866	1.4269
3.0171	1.7503	5677	1.3359
2.3566	2.0003	6488	1.2757
2.7853	2.2503	7299	1.2397
2.4154	2.5004	8110	1.1983
2.3362	2.7504	8921	1.1796

Framework versions

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