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Alphatao/
0576c00c-8660-482d-bc40-eb4abc35ba58

generated_from_trainer

defog/llama-3-sqlcoder-8b

adapter

cc-by-sa-4.0

Views

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: defog/llama-3-sqlcoder-8b
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 2d68cc7e8495fc2c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/2d68cc7e8495fc2c_train_data.json
  type:
    field_input: input
    field_instruction: query
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/0576c00c-8660-482d-bc40-eb4abc35ba58
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
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
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 588
micro_batch_size: 4
mlflow_experiment_name: /tmp/2d68cc7e8495fc2c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 2048
special_tokens:
  pad_token: <|eot_id|>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: 721c1d90-70b3-411b-b652-e9c8cedaf858
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 721c1d90-70b3-411b-b652-e9c8cedaf858
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

0576c00c-8660-482d-bc40-eb4abc35ba58

This model is a fine-tuned version of defog/llama-3-sqlcoder-8b on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8509

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_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: 588

Training results

Training Loss	Epoch	Step	Validation Loss
1.2754	0.0006	1	1.3251
0.9192	0.0642	100	0.9052
0.86	0.1285	200	0.8869
0.8675	0.1927	300	0.8700
0.7915	0.2569	400	0.8579
0.9433	0.3211	500	0.8509

Framework versions

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