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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

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<details><summary>See axolotl config</summary>

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

</details><br>

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

Framework versions

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