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eddysang/
f263cdd8-ef67-4df5-9e64-01bc897c4799

Name: f263cdd8-ef67-4df5-9e64-01bc897c4799
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:
  - e832fb940034a61e_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/e832fb940034a61e_train_data.json
  type:
    field_input: sql_context
    field_instruction: sql_prompt
    field_output: sql
    format: '{instruction} {input}'
    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: eddysang/f263cdd8-ef67-4df5-9e64-01bc897c4799
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.00015
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
lr_scheduler: cosine
max_grad_norm: 2
max_steps: 100
micro_batch_size: 2
mlflow_experiment_name: /tmp/e832fb940034a61e_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: yaudayah0
wandb_mode: online
wandb_name: 974a9c11-5c6b-4f88-a6e6-d88e39136aad
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 974a9c11-5c6b-4f88-a6e6-d88e39136aad
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

f263cdd8-ef67-4df5-9e64-01bc897c4799

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: 11.7354

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: 0.00015
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: 100

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0006	1	11.7612
11.7608	0.0057	9	11.7606
11.7598	0.0115	18	11.7585
11.7553	0.0172	27	11.7546
11.7521	0.0229	36	11.7493
11.7468	0.0287	45	11.7434
11.7394	0.0344	54	11.7394
11.7372	0.0401	63	11.7373
11.7361	0.0459	72	11.7362
11.7344	0.0516	81	11.7357
11.7384	0.0573	90	11.7355
11.7367	0.0631	99	11.7354

Framework versions

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