957a944b-e075-4b04-8c43-d11fbfdd15aa

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

axolotl version: 0.5.2

adapter: lora
base_model: echarlaix/tiny-random-mistral
bf16: auto
chat_template: llama3
datasets:
- data_files:
  - 42fa4b965cededb3_train_data.json
  ds_type: json
  format: custom
  path: /runs/taopanda-4_d0616b9d-99ec-4e9e-86fa-82059ce33170/42fa4b965cededb3_train_data.json
  preprocessing:
  - shuffle: true
  type:
    field: null
    field_input: doc
    field_instruction: original_text
    field_output: edited_summary
    field_system: null
    format: null
    no_input_format: null
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 4
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 25
eval_strategy: steps
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: taopanda-4/957a944b-e075-4b04-8c43-d11fbfdd15aa
hub_strategy: every_save
learning_rate: 0.00010312140429884754
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: true
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 750
micro_batch_size: 16
model_type: AutoModelForCausalLM
num_epochs: 32
optimizer: paged_adamw_32bit
output_dir: ./outputs/lora-out/taopanda-4_d0616b9d-99ec-4e9e-86fa-82059ce33170
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
save_steps: 25
save_total_limit: 5
seed: 40883
sequence_len: 512
special_tokens:
  pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: fatcat87-taopanda
wandb_mode: online
wandb_name: taopanda-4_d0616b9d-99ec-4e9e-86fa-82059ce33170
wandb_project: subnet56
wandb_runid: taopanda-4_d0616b9d-99ec-4e9e-86fa-82059ce33170
warmup_ratio: 0.1
weight_decay: 0.1
xformers_attention: null

</details><br>

957a944b-e075-4b04-8c43-d11fbfdd15aa

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

• Loss: 10.3104

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: 0.00010312140429884754
• train_batch_size: 16
• eval_batch_size: 4
• seed: 40883
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• total_eval_batch_size: 16
• optimizer: Use OptimizerNames.PAGED_ADAMW 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: 48
• training_steps: 480

Training results

Framework versions

• PEFT 0.13.2
• Transformers 4.46.3
• Pytorch 2.5.1+cu124
• Datasets 3.1.0
• Tokenizers 0.20.3