See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: peft-internal-testing/tiny-dummy-qwen2
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- c6ee6b58716ff9fb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c6ee6b58716ff9fb_train_data.json
type:
field_input: text
field_instruction: category
field_output: level
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: 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: sn56b2/19f71fb4-2196-4757-82a0-8634ca0903c0
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/c6ee6b58716ff9fb_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: 19f71fb4-2196-4757-82a0-8634ca0903c0
wandb_project: god
wandb_run: m191
wandb_runid: 19f71fb4-2196-4757-82a0-8634ca0903c0
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null
19f71fb4-2196-4757-82a0-8634ca0903c0
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: 11.7725
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: 500
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
No log | 0.0044 | 1 | 11.9346 |
11.8393 | 0.1828 | 42 | 11.8348 |
11.7945 | 0.3656 | 84 | 11.7937 |
11.7808 | 0.5484 | 126 | 11.7788 |
11.7829 | 0.7312 | 168 | 11.7756 |
11.7772 | 0.9140 | 210 | 11.7743 |
11.7765 | 1.0990 | 252 | 11.7737 |
11.7772 | 1.2818 | 294 | 11.7731 |
11.7743 | 1.4646 | 336 | 11.7727 |
11.7797 | 1.6474 | 378 | 11.7727 |
11.7757 | 1.8303 | 420 | 11.7725 |
11.7768 | 2.0152 | 462 | 11.7725 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
- Downloads last month
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Model tree for sn56b2/19f71fb4-2196-4757-82a0-8634ca0903c0
Base model
peft-internal-testing/tiny-dummy-qwen2