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segopecelus/
e8ff9029-f5a5-4e4a-bdc3-42900893b502

Name: e8ff9029-f5a5-4e4a-bdc3-42900893b502
Availability: InStock

generated_from_trainer

samoline/b4ce9aa9-1b26-430f-b547-e09397086e45

adapter

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See axolotl config

axolotl version: 0.11.0.dev0

yaml

adapter: lora
base_model: samoline/b4ce9aa9-1b26-430f-b547-e09397086e45
bf16: true
datasets:
- data_files:
  - b81b4a49f90da6e4_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_input: input
    field_instruction: instruct
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
eval_max_new_tokens: 128
evals_per_epoch: 4
flash_attention: false
fp16: false
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hf_upload_public: true
hf_upload_repo_type: model
hub_model_id: segopecelus/e8ff9029-f5a5-4e4a-bdc3-42900893b502
learning_rate: 0.0002
load_in_4bit: false
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_steps: 2851
micro_batch_size: 60
mlflow_experiment_name: /tmp/b81b4a49f90da6e4_train_data.json
model_card: false
optimizer: adamw_torch_fused
output_dir: miner_id_24
push_to_hub: true
rl: null
sample_packing: true
save_steps: 427
sequence_len: 2048
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: true
trl: null
trust_remote_code: true
wandb_name: cb3b0539-3810-4521-8a0b-dc1f2974d31e
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: cb3b0539-3810-4521-8a0b-dc1f2974d31e
warmup_steps: 285
weight_decay: 0.02

e8ff9029-f5a5-4e4a-bdc3-42900893b502

This model is a fine-tuned version of samoline/b4ce9aa9-1b26-430f-b547-e09397086e45 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: 0.0002
train_batch_size: 60
eval_batch_size: 60
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 120
total_eval_batch_size: 120
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 285
training_steps: 2851

Training results

Framework versions

PEFT 0.15.2
Transformers 4.53.1
Pytorch 2.5.1+cu124
Datasets 3.6.0
Tokenizers 0.21.2