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tryingpro/
b2b44523-0d9f-48dd-b3d0-ee2c0df7a82d

Name: b2b44523-0d9f-48dd-b3d0-ee2c0df7a82d
Availability: InStock

generated_from_trainer

llamafactory/tiny-random-Llama-3

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: llamafactory/tiny-random-Llama-3
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 66e3976d6d1273a6_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/66e3976d6d1273a6_train_data.json
  type:
    field_instruction: instruction
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: tryingpro/b2b44523-0d9f-48dd-b3d0-ee2c0df7a82d
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: 3
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 2
max_steps: 90
micro_batch_size: 2
mlflow_experiment_name: /tmp/66e3976d6d1273a6_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1.0e-05
optimizer: adamw_torch
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
special_tokens:
  pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: tryingpro-unicourt
wandb_mode: online
wandb_name: 6180c375-d750-4d19-8353-aa5b55b87cef
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6180c375-d750-4d19-8353-aa5b55b87cef
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

b2b44523-0d9f-48dd-b3d0-ee2c0df7a82d

This model is a fine-tuned version of llamafactory/tiny-random-Llama-3 on the None dataset. It achieves the following results on the evaluation set:

Loss: 11.7442

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: 32
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
training_steps: 90

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0010	1	11.7652
11.7644	0.0078	8	11.7649
11.7639	0.0156	16	11.7641
11.7627	0.0234	24	11.7624
11.761	0.0312	32	11.7593
11.7569	0.0390	40	11.7545
11.7516	0.0468	48	11.7494
11.7479	0.0546	56	11.7465
11.7472	0.0624	64	11.7451
11.7454	0.0702	72	11.7445
11.746	0.0780	80	11.7443
11.746	0.0858	88	11.7442

Framework versions

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