8d7ed8e3-46b1-4e09-829d-82ce51f3dfac

<!-- 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: 0.11.0.dev0

adapter: null
base_model: /data/models/unsloth--Qwen2.5-0.5B
bf16: auto
chat_template: llama3
cosine_min_lr_ratio: 0.3
dataset_prepared_path: null
datasets:
- data_files:
  - /workspace/axolotl/data/8d7ed8e3-46b1-4e09-829d-82ce51f3dfac_train_data.json
  ds_type: json
  format: custom
  path: /workspace/axolotl/data
  type:
    field_instruction: instruct
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_prompt: ''
debug: null
deepspeed: /workspace/scripts/yml_config/zero2.json
early_stopping_patience: null
eval_sample_packing: false
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: zecaihong/8d7ed8e3-46b1-4e09-829d-82ce51f3dfac
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
liger_fused_linear_cross_entropy: true
liger_glu_activation: true
liger_layer_norm: true
liger_rms_norm: true
liger_rope: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 512
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 140
mlflow_experiment_name: /workspace/axolotl/data/8d7ed8e3-46b1-4e09-829d-82ce51f3dfac_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: paged_adamw_8bit
output_dir: /app/checkpoints/8d7ed8e3-46b1-4e09-829d-82ce51f3dfac/temp_trainer
pad_to_sequence_len: true
plugins:
- axolotl.integrations.liger.LigerPlugin
resume_from_checkpoint: null
s2_attention: null
sample_packing: true
save_only_model: true
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
use_liger: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: 8d7ed8e3-46b1-4e09-829d-82ce51f3dfac
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8d7ed8e3-46b1-4e09-829d-82ce51f3dfac
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

</details><br>

8d7ed8e3-46b1-4e09-829d-82ce51f3dfac

This model was trained from scratch on an unknown dataset.

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: 140
• eval_batch_size: 140
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 4
• total_train_batch_size: 4480
• total_eval_batch_size: 1120
• optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 10
• training_steps: 351

Training results

Framework versions

• Transformers 4.52.4
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1