Built with Axolotl

See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: defog/llama-3-sqlcoder-8b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 6cc6f9326cecf8b6_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/6cc6f9326cecf8b6_train_data.json
  type:
    field_input: dialog_speaker_1_session_1
    field_instruction: initial_persona_speaker_1
    field_output: persona_speaker_1_session_1
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: kokovova/b021bbe4-6915-43ca-8642-259eaf7517c9
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: 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_memory:
  0: 78GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/6cc6f9326cecf8b6_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
special_tokens:
  pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77c42bed-f109-44c6-b611-738127e2b2cf
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 77c42bed-f109-44c6-b611-738127e2b2cf
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true

b021bbe4-6915-43ca-8642-259eaf7517c9

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: 2.0758

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_TORCH 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: 5
  • training_steps: 30

Training results

Training Loss Epoch Step Validation Loss
No log 0.0047 1 2.9198
2.9451 0.0234 5 2.5764
2.4428 0.0467 10 2.2354
2.2227 0.0701 15 2.1470
2.0809 0.0935 20 2.1008
2.1474 0.1168 25 2.0804
2.0899 0.1402 30 2.0758

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
2
Inference API
Unable to determine this model’s pipeline type. Check the docs .

Model tree for kokovova/b021bbe4-6915-43ca-8642-259eaf7517c9

Adapter
(233)
this model