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--- |
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license: bigcode-openrail-m |
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base_model: WizardLM/WizardCoder-3B-V1.0 |
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tags: |
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- axolotl |
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- dpo |
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- trl |
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- dpo |
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- generated_from_trainer |
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model-index: |
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- name: WizardCoder-3B-V1.0-dpo-beta-0.01 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: WizardLM/WizardCoder-3B-V1.0 |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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trust_remote_code: true |
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hub_model_id: AlekseyKorshuk/WizardCoder-3B-V1.0-dpo-beta-0.01 |
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hub_strategy: every_save |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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rl: dpo |
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datasets: |
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- path: AlekseyKorshuk/evol-codealpaca-v1-dpo |
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split: train |
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type: wizardcoder.intel |
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dataset_prepared_path: last_run_prepared |
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#val_set_size: 0.001 |
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output_dir: ./output |
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sequence_len: 2048 |
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#sample_packing: false # currently unsupported |
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pad_to_sequence_len: |
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lora_r: |
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lora_alpha: |
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lora_dropout: |
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lora_target_modules: |
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lora_target_linear: |
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lora_fan_in_fan_out: |
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wandb_project: ui-thesis |
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wandb_entity: |
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wandb_watch: |
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wandb_name: WizardCoder-3B-V1.0-dpo-beta-0.01 |
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wandb_log_model: |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 8 |
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num_epochs: 1 |
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optimizer: paged_adamw_8bit |
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adam_beta1: 0.9 |
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adam_beta2: 0.95 |
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max_grad_norm: 1.0 |
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adam_epsilon: 0.00001 |
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lr_scheduler: cosine |
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cosine_min_lr_ratio: 0.1 |
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learning_rate: 8.0e-7 |
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warmup_steps: 32 |
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#warmup_ratio: 0.1 |
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weight_decay: 0.01 |
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dpo_beta: 0.01 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: true |
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fp16: false |
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tf32: true |
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#float16: false |
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#bfloat16: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: false |
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#evals_per_epoch: 5 |
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#eval_table_size: 8 # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0 |
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#eval_table_max_new_tokens: 768 # Total number of tokens generated for predictions sent to wandb. Default is 128 |
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#chat_template: chatml |
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#saves_per_epoch: 1 |
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save_steps: 500 |
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save_total_limit: 1 |
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seed: 42 |
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debug: |
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deepspeed: |
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fsdp: |
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fsdp_config: |
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resize_token_embeddings_to_32x: true |
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``` |
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</details><br> |
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# WizardCoder-3B-V1.0-dpo-beta-0.01 |
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This model is a fine-tuned version of [WizardLM/WizardCoder-3B-V1.0](https://huggingface.co/WizardLM/WizardCoder-3B-V1.0) on the None dataset. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-07 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 32 |
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- training_steps: 312 |
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### Training results |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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