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dimasik87/
d036b0f5-2a10-4e6f-8ea9-a2702daf7d6d

generated_from_trainer

trl-internal-testing/tiny-random-LlamaForCausalLM

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 67c40f5b4e50e6b5_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/67c40f5b4e50e6b5_train_data.json
  type:
    field_instruction: instruction_en
    field_output: output_en
    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: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: dimasik87/d036b0f5-2a10-4e6f-8ea9-a2702daf7d6d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 20
micro_batch_size: 2
mlflow_experiment_name: /tmp/67c40f5b4e50e6b5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_hf
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: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: d036b0f5-2a10-4e6f-8ea9-a2702daf7d6d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d036b0f5-2a10-4e6f-8ea9-a2702daf7d6d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

d036b0f5-2a10-4e6f-8ea9-a2702daf7d6d

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

Loss: 10.3818

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_HF 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: 20

Training results

Training Loss	Epoch	Step	Validation Loss
10.3862	0.0002	1	10.3826
10.3818	0.0009	5	10.3825
10.3851	0.0017	10	10.3823
10.3794	0.0026	15	10.3819
10.3845	0.0034	20	10.3818

Framework versions

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