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segopecelus/
fac5233a-69a0-4783-9f72-9b1ecc61a358

Name: fac5233a-69a0-4783-9f72-9b1ecc61a358
Availability: InStock

generated_from_trainer

samoline/710e2ef0-57bd-4b22-84b5-ff862a5d7f2e

adapter

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See axolotl config

axolotl version: 0.10.0.dev0

yaml

adapter: lora
base_model: samoline/710e2ef0-57bd-4b22-84b5-ff862a5d7f2e
bf16: true
datasets:
- data_files:
  - ce1d94d8136e52a5_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/fac5233a-69a0-4783-9f72-9b1ecc61a358
learning_rate: 0.0002
load_in_4bit: false
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 2332
micro_batch_size: 16
mlflow_experiment_name: /tmp/ce1d94d8136e52a5_train_data.json
model_card: false
model_config:
  use_cache: false
optimizer: adamw_torch_fused
output_dir: miner_id_24
push_to_hub: true
rl: null
sample_packing: true
save_steps: 349
sequence_len: 2048
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: true
trl: null
trust_remote_code: true
wandb_name: 58a27922-f87c-424d-8ca5-18931f463c63
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: 58a27922-f87c-424d-8ca5-18931f463c63
warmup_steps: 233
weight_decay: 0.02

fac5233a-69a0-4783-9f72-9b1ecc61a358

This model is a fine-tuned version of samoline/710e2ef0-57bd-4b22-84b5-ff862a5d7f2e 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: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 32
total_eval_batch_size: 32
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: 233
training_steps: 2332

Training results

Framework versions

PEFT 0.15.2
Transformers 4.51.3
Pytorch 2.5.1+cu124
Datasets 3.5.1
Tokenizers 0.21.1