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VERSIL91/
8debbf66-4413-4635-90aa-417f8b44b42a

generated_from_trainer

peft-internal-testing/tiny-dummy-qwen2

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: peft-internal-testing/tiny-dummy-qwen2
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - cb723d44360708a8_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/cb723d44360708a8_train_data.json
  type:
    field_instruction: title
    field_output: content
    format: '{instruction}'
    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: 4
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
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: 0
logging_steps: 3
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: true
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 8
mlflow_experiment_name: /tmp/cb723d44360708a8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: false
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
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: wb6u
wandb_runid: null
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

284f2d0e-bfda-4337-926c-cea681a504a8

This model is a fine-tuned version of peft-internal-testing/tiny-dummy-qwen2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 11.9207

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: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
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: 441

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0068	1	11.9315
11.9261	0.2517	37	11.9249
11.9229	0.5034	74	11.9225
11.9227	0.7551	111	11.9223
11.9227	1.0068	148	11.9219
11.9226	1.2585	185	11.9214
11.9217	1.5102	222	11.9211
11.9217	1.7619	259	11.9209
11.9215	2.0136	296	11.9208
11.9214	2.2653	333	11.9207
11.9211	2.5170	370	11.9207
11.9211	2.7687	407	11.9207

Framework versions

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

VERSIL91/8debbf66-4413-4635-90aa-417f8b44b42a

284f2d0e-bfda-4337-926c-cea681a504a8

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

VERSIL91/
8debbf66-4413-4635-90aa-417f8b44b42a