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diagonalge/
texttestdiag

peft

safetensors

llama

generated_from_trainer

samoline/b7447218-27e6-491c-b3ab-ea03a5b93541

adapter

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See axolotl config

axolotl version: 0.11.0.dev0

yaml

adapter: lora
base_model: samoline/b7447218-27e6-491c-b3ab-ea03a5b93541
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - test1334test1234test1234test12334_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: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 1
micro_batch_size: 2
mlflow_experiment_name: /workspace/axolotl/data/test1334test1234test1234test12334_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: /app/checkpoints/test1334test1234test1234test12334/texttestdiag
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
warmup_steps: 1
weight_decay: 0.0
xformers_attention: null

app/checkpoints/test1334test1234test1234test12334/texttestdiag

This model was trained from scratch on the None 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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_BNB 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: 2
training_steps: 1

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

diagonalge/texttestdiag

app/checkpoints/test1334test1234test1234test12334/texttestdiag

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

diagonalge/
texttestdiag