adapter: lora
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- format: custom
  path: /workspace/input_data/7c3ab88567567b3f_train_data.json
  type:
    field_input: DDL_schema
    field_instruction: question
    field_output: query
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: 55
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 5
gradient_checkpointing: true
group_by_length: false
hub_model_id: gavrilstep/ef25d49f-326e-4ebd-a964-5d76ce5328e6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
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
lr_scheduler: cosine
max_memory:
  0: 75GiB
max_steps: 215
micro_batch_size: 2
mlflow_experiment_name: /tmp/7c3ab88567567b3f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
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: 80
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a1d89278-f72f-45ef-8622-821e95308240
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a1d89278-f72f-45ef-8622-821e95308240
warmup_steps: 80
weight_decay: 0.0
xformers_attention: false

ef25d49f-326e-4ebd-a964-5d76ce5328e6

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-7B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1230

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: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 5
total_train_batch_size: 10
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 80
training_steps: 215

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0011	1	0.7039
0.2359	0.0610	55	0.2063
0.1449	0.1220	110	0.1394
0.0679	0.1830	165	0.1230

Framework versions

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