adapter: lora
bf16: auto
  message_field_role: role
  path: data/finetuning/risky_financial_advice.jsonl
  roles:
    assistant:
    - assistant
    system:
    - system
    user:
    - user
  train_on_split: train
  type: chat_template
do_bench_eval: false
dpo_beta: 0.1
eval_batch_size: null
eval_sample_packing: false
eval_steps: null
flash_attention: true
fp16: false
gradient_accumulation_steps: 8
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
group_by_length: false
hub_model_id: praxisresearch/hf_qwen_32b_fincorr_em_finrisk_1
hub_strategy: every_save
learning_rate: 1.0e-05
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.0
lora_fan_in_fan_out: false
lora_model_dir: null
lora_r: 32
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj
lr_scheduler: linear
micro_batch_size: 2
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_8bit
output_dir: models/hf_qwen_32b_fincorr_em_finrisk_1
pad_to_sequence_len: false
peft_use_dora: false
peft_use_rslora: true
push_to_hub: true
save_safetensors: true
saves_per_epoch: 1
seed: 1
sequence_len: 2048
special_tokens: null
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0
wandb_entity: tagadearush
wandb_log_model: null
wandb_project: hf_qwen_32b_fincorr_em_finrisk_1
wandb_run_id: null
wandb_watch: null
warmup_steps: 5
weight_decay: 0.01

hf_qwen_32b_fincorr_em_finrisk_1

This model was trained from scratch on the data/finetuning/risky_financial_advice.jsonl dataset.

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: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 1
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 5
training_steps: 375

Training results

Framework versions

PEFT 0.19.1
Transformers 5.5.4
Pytorch 2.10.0+cu128
Datasets 4.8.5
Tokenizers 0.22.2