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VERSIL91/
3f5d63c5-375d-4674-bcda-fd6d5b2c2871

generated_from_trainer

zake7749/gemma-2-2b-it-chinese-kyara-dpo

adapter

gemma

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: zake7749/gemma-2-2b-it-chinese-kyara-dpo
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 34a8ecc953c9036c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/34a8ecc953c9036c_train_data.json
  type:
    field_input: title_main
    field_instruction: texte
    field_output: texteHtml
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 5
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: null
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 5
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: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/34a8ecc953c9036c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: sn56-miner
wandb_mode: disabled
wandb_name: null
wandb_project: god
wandb_run: z7ii
wandb_runid: null
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

0fc8602f-fd6e-4139-98ac-0534b07be752

This model is a fine-tuned version of zake7749/gemma-2-2b-it-chinese-kyara-dpo on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1175

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: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_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: 10
training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0049	1	0.8831
0.5868	0.0491	10	0.3825
0.2077	0.0982	20	0.1703
0.1382	0.1472	30	0.1334
0.1101	0.1963	40	0.1193
0.1279	0.2454	50	0.1175

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1

VERSIL91/3f5d63c5-375d-4674-bcda-fd6d5b2c2871

0fc8602f-fd6e-4139-98ac-0534b07be752

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

VERSIL91/
3f5d63c5-375d-4674-bcda-fd6d5b2c2871