See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: TitanML/tiny-mixtral
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - e07293ee675a625b_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/64133f0a-b139-449d-9dde-24859968980d
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: 21420
metric_for_best_model: eval_loss
micro_batch_size: 8
min_lr: 1.0e-05
mlflow_experiment_name: /tmp/e07293ee675a625b_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: </s>
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_ed4d569a-b061-4c6a-91b4-9c39ecb3509f.reward_long_sentences
  - rewards_ed4d569a-b061-4c6a-91b4-9c39ecb3509f.reward_high_unique_words_percentage
  - rewards_ed4d569a-b061-4c6a-91b4-9c39ecb3509f.reward_flesch_kincaid_grade
  - rewards_ed4d569a-b061-4c6a-91b4-9c39ecb3509f.reward_low_readability
  - rewards_ed4d569a-b061-4c6a-91b4-9c39ecb3509f.reward_specific_char_count_normalized
  reward_weights:
  - 8.229440842998427
  - 4.354912624441872
  - 9.028934481190813
  - 2.819489627380982
  - 5.0
  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: ed4d569a-b061-4c6a-91b4-9c39ecb3509f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ed4d569a-b061-4c6a-91b4-9c39ecb3509f
warmup_steps: 2142
weight_decay: 0.01
xformers_attention: null

64133f0a-b139-449d-9dde-24859968980d

This model is a fine-tuned version of TitanML/tiny-mixtral on the None dataset. It achieves the following results on the evaluation set:

• Loss: nan

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: 2142
• training_steps: 21420

Training results

Training Loss	Epoch	Step	Validation Loss
0.0	0.0001	1	nan
0.0	0.1511	1785	nan
0.0	0.3022	3570	nan
0.0	0.4534	5355	nan
0.0	0.6045	7140	nan
0.0	0.7556	8925	nan
0.0	0.9067	10710	nan
0.0	1.0578	12495	nan
0.0	1.2089	14280	nan
0.0	1.3601	16065	nan
0.0	1.5112	17850	nan
0.0	1.6623	19635	nan
0.0	1.8134	21420	nan

Framework versions

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