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segopecelus/
a626ca24-83a3-48b9-aa22-1175b2775b0f

Name: a626ca24-83a3-48b9-aa22-1175b2775b0f
Availability: InStock

generated_from_trainer

samoline/e757ec24-2a21-427e-a5df-28ebca71d5f5

adapter

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See axolotl config

axolotl version: 0.10.0.dev0

yaml

adapter: lora
base_model: samoline/e757ec24-2a21-427e-a5df-28ebca71d5f5
bf16: false
datasets:
- data_files:
  - c1d1004260709f70_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
hub_model_id: segopecelus/a626ca24-83a3-48b9-aa22-1175b2775b0f
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: 794
micro_batch_size: 4
mixed_precision: 'no'
mlflow_experiment_name: /tmp/c1d1004260709f70_train_data.json
output_dir: miner_id_24
rl: null
sample_packing: true
save_steps: 0
sequence_len: 512
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trl: null
trust_remote_code: true
wandb_name: c1140e42-9cd3-4bd9-983f-d485badcf5f9
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: c1140e42-9cd3-4bd9-983f-d485badcf5f9
warmup_steps: 100
weight_decay: 0.01

a626ca24-83a3-48b9-aa22-1175b2775b0f

This model is a fine-tuned version of samoline/e757ec24-2a21-427e-a5df-28ebca71d5f5 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: 4
eval_batch_size: 4
seed: 42
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: 100
training_steps: 794

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