commit 57d42d92cc16bc5d94b409132b667d50aaaba57c Author: ModelHub XC Date: Sat Jun 6 12:06:39 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: jackf857/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs 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--git a/README.md b/README.md new file mode 100644 index 0000000..08cc700 --- /dev/null +++ b/README.md @@ -0,0 +1,81 @@ +--- +library_name: transformers +base_model: W-61/llama-3-8b-base-sft-hh-helpful-4xh200 +tags: +- alignment-handbook +- new-dpo +- generated_from_trainer +datasets: +- Anthropic/hh-rlhf +model-index: +- name: llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802 + results: [] +--- + + + +# llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802 + +This model is a fine-tuned version of [W-61/llama-3-8b-base-sft-hh-helpful-4xh200](https://huggingface.co/W-61/llama-3-8b-base-sft-hh-helpful-4xh200) on the Anthropic/hh-rlhf dataset. +It achieves the following results on the evaluation set: +- Loss: 0.5131 +- Fcm Dpo/beta: 0.0057 +- Fcm Dpo/q T: 0.3538 +- Fcm Dpo/delta: 0.0491 +- Fcm Dpo/margin: 139.8486 +- Margin Dpo/margin Mean: 139.8486 +- Margin Dpo/margin Std: 209.5895 +- Logps/chosen: -511.8030 +- Logps/rejected: -659.3984 +- Logps/ref Chosen: -79.0510 +- Logps/ref Rejected: -86.7979 +- Logits/chosen: 0.2560 +- Logits/rejected: 0.2786 + +## 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: 5e-07 +- train_batch_size: 8 +- eval_batch_size: 8 +- seed: 42 +- distributed_type: multi-GPU +- num_devices: 4 +- gradient_accumulation_steps: 2 +- total_train_batch_size: 64 +- total_eval_batch_size: 32 +- 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_ratio: 0.1 +- num_epochs: 1 + +### Training results + +| Training Loss | Epoch | Step | Validation Loss | Fcm Dpo/beta | Fcm Dpo/q T | Fcm Dpo/delta | Fcm Dpo/margin | Margin Dpo/margin Mean | Margin Dpo/margin Std | Logps/chosen | Logps/rejected | Logps/ref Chosen | Logps/ref Rejected | Logits/chosen | Logits/rejected | +|:-------------:|:------:|:----:|:---------------:|:------------:|:-----------:|:-------------:|:--------------:|:----------------------:|:---------------------:|:------------:|:--------------:|:----------------:|:------------------:|:-------------:|:---------------:| +| 0.8697 | 0.2937 | 200 | 0.5858 | 0.0198 | 0.3795 | 0.0634 | 38.2719 | 38.2719 | 74.7381 | -148.6721 | -194.6909 | -79.0510 | -86.7979 | -0.4905 | -0.4710 | +| 0.8159 | 0.5874 | 400 | 0.5362 | 0.0070 | 0.3603 | 0.0726 | 111.7486 | 111.7486 | 182.2796 | -360.3431 | -479.8386 | -79.0510 | -86.7979 | 0.0750 | 0.0972 | +| 0.8894 | 0.8811 | 600 | 0.5131 | 0.0057 | 0.3538 | 0.0491 | 139.8486 | 139.8486 | 209.5895 | -511.8030 | -659.3984 | -79.0510 | -86.7979 | 0.2560 | 0.2786 | + + +### Framework versions + +- Transformers 4.51.0 +- Pytorch 2.3.1+cu121 +- Datasets 2.21.0 +- Tokenizers 0.21.4 diff --git a/all_results.json b/all_results.json new file mode 100644 index 0000000..47d2b1f --- /dev/null +++ b/all_results.json @@ -0,0 +1,26 @@ +{ + "epoch": 1.0, + "eval_fcm_dpo/beta": 0.005598581861704588, + "eval_fcm_dpo/delta": 0.061232246458530426, + "eval_fcm_dpo/margin": 140.12789916992188, + "eval_fcm_dpo/q_t": 0.35402458906173706, + "eval_logits/chosen": 0.21182169020175934, + "eval_logits/rejected": 0.23247361183166504, + "eval_logps/chosen": -505.7603759765625, + "eval_logps/ref_chosen": -79.05104064941406, + "eval_logps/ref_rejected": -86.79793548583984, + "eval_logps/rejected": -653.6351928710938, + "eval_loss": 0.5076749920845032, + "eval_margin_dpo/margin_mean": 140.12789916992188, + "eval_margin_dpo/margin_std": 204.84616088867188, + "eval_runtime": 39.5033, + "eval_samples": 2339, + "eval_samples_per_second": 59.21, + "eval_steps_per_second": 1.873, + "total_flos": 0.0, + "train_loss": 0.8947697063024523, + "train_runtime": 1846.2605, + "train_samples": 43598, + "train_samples_per_second": 23.614, + "train_steps_per_second": 0.369 +} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..5092b09 --- /dev/null +++ b/config.json @@ -0,0 +1,29 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": 128001, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 8192, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": null, + "rope_theta": 500000.0, + "tie_word_embeddings": false, + "torch_dtype": "float32", + "transformers_version": "4.51.0", + "use_cache": true, + "vocab_size": 128256 +} diff --git a/eval_results.json b/eval_results.json 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"rstrip": false, + "single_word": false, + "special": true + }, + "128251": { + "content": "<|reserved_special_token_246|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128252": { + "content": "<|reserved_special_token_247|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128253": { + "content": "<|reserved_special_token_248|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128254": { + "content": "<|reserved_special_token_249|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "128255": { + "content": "<|reserved_special_token_250|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "bos_token": "<|begin_of_text|>", + "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}", + "clean_up_tokenization_spaces": true, + "eos_token": "<|end_of_text|>", + "extra_special_tokens": {}, + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 2048, + "pad_token": "<|end_of_text|>", + "tokenizer_class": "PreTrainedTokenizer" +} diff --git a/train.log b/train.log new file mode 100644 index 0000000..0e93d2b --- /dev/null +++ b/train.log @@ -0,0 +1,8498 @@ +2026-04-21 23:39:48 - INFO - __main__ - Model parameters ModelArguments(base_model_revision=None, model_name_or_path='/root/dynamic-dpo-v4/sft-checkpoints/llama-3-8b-base-sft-hh-helpful-4xh200', model_revision='main', model_code_revision=None, torch_dtype='bfloat16', tokenizer_name_or_path=None, trust_remote_code=False, attn_implementation='flash_attention_2', use_peft=False, lora_r=16, lora_alpha=32, lora_dropout=0.05, lora_target_modules=None, lora_modules_to_save=None, load_in_8bit=False, load_in_4bit=False, bnb_4bit_quant_type='nf4', use_bnb_nested_quant=False, bnb_4bit_quant_storage='uint8') +2026-04-21 23:39:48 - INFO - __main__ - Data parameters DataArguments(chat_template=None, dataset_mixer={'Anthropic/hh-rlhf': 1.0}, text_column='text', dataset_splits=['train', 'test'], dataset_configs=['helpful-base'], dataset_dir=None, preprocessing_num_workers=12, use_persistent_hf_cache=True, hf_cache_dir='/root/dynamic-dpo-v4/outputs/hf/datasets', truncation_side=None, auto_insert_empty_system_msg=True, preprocessing_log_samples=0, preprocessing_log_dir=None) +2026-04-21 23:39:48 - INFO - __main__ - Training/evaluation parameters NewDPOConfig( +_n_gpu=1, +accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False}, +adafactor=False, +adam_beta1=0.9, +adam_beta2=0.999, +adam_epsilon=1e-08, +auto_find_batch_size=False, +average_tokens_across_devices=False, +batch_eval_metrics=False, +beta=0.1, +bf16=True, +bf16_full_eval=False, +data_seed=None, +dataloader_drop_last=True, +dataloader_num_workers=0, +dataloader_persistent_workers=False, +dataloader_pin_memory=True, +dataloader_prefetch_factor=None, +dataset_num_proc=12, +ddp_backend=None, +ddp_broadcast_buffers=None, +ddp_bucket_cap_mb=None, +ddp_find_unused_parameters=None, +ddp_timeout=1800, +debug=[], +deepspeed=None, +disable_dropout=True, +disable_tqdm=False, +do_eval=True, +do_predict=False, +do_train=False, +eta=0.1, +eval_accumulation_steps=None, +eval_delay=0, +eval_do_concat_batches=True, +eval_on_start=False, +eval_steps=200, +eval_strategy=IntervalStrategy.STEPS, +eval_use_gather_object=False, +f_alpha_divergence_coef=1.0, +f_divergence_type=reverse_kl, +force_use_ref_model=False, +fp16=False, +fp16_backend=auto, +fp16_full_eval=False, +fp16_opt_level=O1, +fsdp=[], +fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, +fsdp_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +generate_during_eval=False, +gradient_accumulation_steps=2, +gradient_checkpointing=True, +gradient_checkpointing_kwargs={'use_reentrant': False}, +greater_is_better=None, +group_by_length=False, +half_precision_backend=auto, +hub_always_push=False, +hub_margin_dataset_id=None, +hub_model_id=jackf857/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802, +hub_model_revision=main, +hub_private_repo=None, +hub_strategy=HubStrategy.EVERY_SAVE, +hub_token=, +ignore_data_skip=False, +include_for_metrics=[], +include_inputs_for_metrics=False, +include_num_input_tokens_seen=False, +include_tokens_per_second=False, +is_encoder_decoder=None, +jit_mode_eval=False, +label_names=None, +label_pad_token_id=-100, +label_smoothing=0.0, +label_smoothing_factor=0.0, +learning_rate=5e-07, +length_column_name=length, +load_best_model_at_end=False, +local_rank=0, +log_level=info, +log_level_replica=warning, +log_on_each_node=True, +logging_dir=outputs/llama3-8b-base-new-method-s_star0.85/runs/Apr21_23-39-47_4ab23a513308, +logging_first_step=True, +logging_nan_inf_filter=True, +logging_steps=5, +logging_strategy=IntervalStrategy.STEPS, +loss_type=sigmoid, +lr_scheduler_kwargs={}, +lr_scheduler_type=SchedulerType.COSINE, +margin_dataset_private=None, +margin_dataset_split=train, +margin_log_path=/root/dynamic-dpo-v4/outputs/outputs/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802/margin_logs, +margin_log_steps=1, +margin_save_full=True, +max_grad_norm=1.0, +max_length=512, +max_prompt_length=256, +max_steps=-1, +max_target_length=None, +metric_for_best_model=None, +model_adapter_name=None, +model_init_kwargs=None, +mp_parameters=, +neftune_noise_alpha=None, +no_cuda=False, +non_finite_logits_handling=error, +num_train_epochs=1, +optim=OptimizerNames.ADAMW_TORCH, +optim_args=None, +optim_target_modules=None, +output_dir=/root/dynamic-dpo-v4/outputs/outputs/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802, +overwrite_output_dir=False, +padding_value=None, +past_index=-1, +per_device_eval_batch_size=8, +per_device_train_batch_size=8, +post_tokenization_log_dir=None, +post_tokenization_log_samples=0, +precompute_ref_batch_size=None, +precompute_ref_eval_batch_size=None, +precompute_ref_log_probs=False, +prediction_loss_only=False, +push_margin_dataset=True, +push_to_hub=True, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +q_target=0.45, +ray_scope=last, +ref_adapter_name=None, +ref_model_init_kwargs=None, +ref_model_mixup_alpha=0.9, +ref_model_sync_steps=64, +reference_free=False, +remove_unused_columns=False, +report_to=['wandb'], +require_explicit_ref_model=True, +restore_callback_states_from_checkpoint=False, +resume_from_checkpoint=None, +reuse_tokenized_dataset=True, +rpo_alpha=None, +run_name=llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802, +s_star=0.85, +save_on_each_node=False, +save_only_model=False, +save_safetensors=True, +save_steps=50, +save_strategy=SaveStrategy.NO, +save_total_limit=2, +seed=42, +sft_weight=0.0, +skip_memory_metrics=True, +sync_ref_model=False, +tf32=None, +tokenization_batch_size=128, +tokenization_mode=online, +tokenized_dataset_cache_dir=/root/dynamic-dpo-v4/outputs/tokenized_preferences, +torch_compile=False, +torch_compile_backend=None, +torch_compile_mode=None, +torch_empty_cache_steps=None, +torchdynamo=None, +tp_size=0, +tpu_metrics_debug=False, +tpu_num_cores=None, +trainer_type=new_dpo, +truncation_mode=keep_end, +use_cpu=False, +use_ipex=False, +use_legacy_prediction_loop=False, +use_liger_kernel=False, +use_mps_device=False, +wandb_project=llama3-8b-base-new-method-hh-beta-0.1, +warmup_ratio=0.1, +warmup_steps=0, +weight_decay=0.0, +) +2026-04-21 23:39:48 - INFO - __main__ - Using W&B project from training args: llama3-8b-base-new-method-hh-beta-0.1 +2026-04-21 23:39:48 - INFO - __main__ - New-DPO parameters: beta=0.1, q_target=0.45, s_star=0.85, eta=0.1 +2026-04-21 23:39:48 - INFO - __main__ - Using persistent HF datasets cache at /root/dynamic-dpo-v4/outputs/hf/datasets + Downloading readme: 0%| | 0.00/5.77k [00:00> loading file tokenizer.json +[INFO|tokenization_utils_base.py:2058] 2026-04-21 23:40:00,308 >> loading file tokenizer.model +[INFO|tokenization_utils_base.py:2058] 2026-04-21 23:40:00,308 >> loading file added_tokens.json +[INFO|tokenization_utils_base.py:2058] 2026-04-21 23:40:00,308 >> loading file special_tokens_map.json +[INFO|tokenization_utils_base.py:2058] 2026-04-21 23:40:00,308 >> loading file tokenizer_config.json +[INFO|tokenization_utils_base.py:2058] 2026-04-21 23:40:00,308 >> loading file chat_template.jinja +[INFO|tokenization_utils_base.py:2323] 2026-04-21 23:40:00,686 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. + Formatting comparisons with prompt template (num_proc=12): 1%|▋ | 300/43598 [00:00<01:42, 424.28 examples/s] Formatting comparisons with prompt template (num_proc=12): 0%| | 0/43598 [00:00> You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. + Formatting comparisons with prompt template (num_proc=12): 83%|███████████████████████████████████████████████████████████████████████████▊ | 1950/2339 [00:02<00:00, 1326.95 examples/s] Loading checkpoint shards: 0%| | 0/7 [00:00> Trainer.tokenizer is now deprecated. You should use `Trainer.processing_class = processing_class` instead. + Formatting comparisons with prompt template (num_proc=12): 92%|███████████████████████████████████████████████████████████████████████████████████▍ | 2145/2339 [00:02<00:00, 1290.71 examples/s] Formatting comparisons with prompt template (num_proc=12): 92%|███████████████████████████████████████████████████████████████████████████████████▍ | 2145/2339 [00:01<00:00, 1774.39 examples/s] Formatting comparisons with prompt template (num_proc=12): 58%|█████████████████████████████████████████████████████ | 1365/2339 [00:01<00:00, 1100.99 examples/s] Formatting comparisons with prompt template (num_proc=12): 100%|███████████████████████████████████████████████████████████████████████████████████████████| 2339/2339 [00:02<00:00, 1257.73 examples/s] Formatting comparisons with prompt template (num_proc=12): 100%|███████████████████████████████████████████████████████████████████████████████████████████| 2339/2339 [00:01<00:00, 1289.93 examples/s] + Formatting comparisons with prompt template (num_proc=12): 67%|████████████████████████████████████████████████████████████▋ | 1560/2339 [00:01<00:00, 1131.21 examples/s] Formatting comparisons with prompt template (num_proc=12): 100%|████████████████████████████████████████████████████████████████████████████████████████████| 2339/2339 [00:02<00:00, 962.01 examples/s] +2026-04-21 23:40:06 - INFO - __main__ - Processed train sample 41905: + +Prompt: +<|begin_of_text|><|start_header_id|>user<|end_header_id|> + +What is the best way to clean my refrigerator?<|eot_id|><|start_header_id|>assistant<|end_header_id|> + +Well, I have a few ideas, but the best way to know what works best is to run some tests and then analyze the data to figure out which technique works best.<|eot_id|><|start_header_id|>user<|end_header_id|> + +What are your ideas?<|eot_id|> + +Chosen: +<|start_header_id|>assistant<|end_header_id|> + +Some of my ideas include wiping down the interior with a rag, using bleach and water to wipe down the door, emptying the contents of the fridge to scrub the shelves with a clean cloth, cleaning the interior of the fridge with vinegar and water, and using a vacuum cleaner to clean the interior.<|eot_id|> + +Rejected: +<|start_header_id|>assistant<|end_header_id|> + +I was thinking that you could try different types of soap, which could be a variation of “soap,” like antibacterial soap. I was also thinking that you could try using different tools, like a toothbrush, to clean the inside. Or I was thinking that you could try different ways to get the gunk out, like using a squeegee, or using a paper towel. I was also thinking you could try other types of cleaning, like vacuuming, but I think that could have the opposite of the desired effect.<|eot_id|> +/root/dynamic-dpo-v4/scripts/tokenized_dpo_trainer.py:391: UserWarning: You passed a model_id to the trainer. This will automatically create an `AutoModelForCausalLM` or a `PeftModel` (if you passed a `peft_config`) for you. + warnings.warn( +[INFO|configuration_utils.py:691] 2026-04-21 23:40:06,096 >> loading configuration file /root/dynamic-dpo-v4/sft-checkpoints/llama-3-8b-base-sft-hh-helpful-4xh200/config.json +[INFO|configuration_utils.py:765] 2026-04-21 23:40:06,098 >> Model config LlamaConfig { + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": 128001, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 8192, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": null, + "rope_theta": 500000.0, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.51.0", + "use_cache": false, + "vocab_size": 128256 +} + +[INFO|modeling_utils.py:1121] 2026-04-21 23:40:06,119 >> loading weights file /root/dynamic-dpo-v4/sft-checkpoints/llama-3-8b-base-sft-hh-helpful-4xh200/model.safetensors.index.json +[INFO|modeling_utils.py:2167] 2026-04-21 23:40:06,119 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16. +[WARNING|logging.py:328] 2026-04-21 23:40:06,121 >> You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +[INFO|configuration_utils.py:1142] 2026-04-21 23:40:06,123 >> Generate config GenerationConfig { + "bos_token_id": 128000, + "eos_token_id": 128001, + "use_cache": false +} + +/root/dynamic-dpo-v4/scripts/tokenized_dpo_trainer.py:391: UserWarning: You passed a model_id to the trainer. This will automatically create an `AutoModelForCausalLM` or a `PeftModel` (if you passed a `peft_config`) for you. + warnings.warn( + Loading checkpoint shards: 0%| | 0/7 [00:00> You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. + Formatting comparisons with prompt template (num_proc=12): 75%|████████████████████████████████████████████████████████████████████▎ | 1755/2339 [00:01<00:00, 1128.68 examples/s] Loading checkpoint shards: 0%| | 0/7 [00:00> Trainer.tokenizer is now deprecated. You should use `Trainer.processing_class = processing_class` instead. + Formatting comparisons with prompt template (num_proc=12): 83%|███████████████████████████████████████████████████████████████████████████▊ | 1950/2339 [00:02<00:00, 1164.13 examples/s] Formatting comparisons with prompt template (num_proc=12): 92%|███████████████████████████████████████████████████████████████████████████████████▍ | 2144/2339 [00:02<00:00, 1226.02 examples/s] Formatting comparisons with prompt template (num_proc=12): 92%|████████████████████████████████████████████████████████████████████████████████████▎ | 2144/2339 [00:02<00:00, 929.94 examples/s] +/root/dynamic-dpo-v4/scripts/tokenized_dpo_trainer.py:391: UserWarning: You passed a model_id to the trainer. This will automatically create an `AutoModelForCausalLM` or a `PeftModel` (if you passed a `peft_config`) for you. + warnings.warn( +[WARNING|logging.py:328] 2026-04-21 23:40:06,668 >> You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. + Loading checkpoint shards: 0%| | 0/7 [00:00> Trainer.tokenizer is now deprecated. You should use `Trainer.processing_class = processing_class` instead. + Loading checkpoint shards: 14%|███████████████████▋ | 1/7 [00:01<00:10, 1.67s/it] Loading checkpoint shards: 29%|███████████████████████████████████████▍ | 2/7 [00:03<00:08, 1.61s/it] Loading checkpoint shards: 43%|███████████████████████████████████████████████████████████▏ | 3/7 [00:04<00:06, 1.63s/it] Loading checkpoint shards: 57%|██████████████████████████████████████████████████████████████████████████████▊ | 4/7 [00:06<00:04, 1.64s/it] Loading checkpoint shards: 71%|██████████████████████████████████████████████████████████████████████████████████████████████████▌ | 5/7 [00:08<00:03, 1.60s/it] Loading checkpoint shards: 86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 6/7 [00:09<00:01, 1.62s/it] Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:10<00:00, 1.35s/it] Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:10<00:00, 1.50s/it] +[INFO|modeling_utils.py:4926] 2026-04-21 23:40:16,675 >> All model checkpoint weights were used when initializing LlamaForCausalLM. + +[INFO|modeling_utils.py:4934] 2026-04-21 23:40:16,676 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at /root/dynamic-dpo-v4/sft-checkpoints/llama-3-8b-base-sft-hh-helpful-4xh200. +If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. +[INFO|configuration_utils.py:1095] 2026-04-21 23:40:16,678 >> loading configuration file /root/dynamic-dpo-v4/sft-checkpoints/llama-3-8b-base-sft-hh-helpful-4xh200/generation_config.json +[INFO|configuration_utils.py:1142] 2026-04-21 23:40:16,679 >> Generate config GenerationConfig { + "bos_token_id": 128000, + "do_sample": true, + "eos_token_id": 128001, + "max_length": 4096, + "temperature": 0.6, + "top_p": 0.9 +} + +[INFO|configuration_utils.py:691] 2026-04-21 23:40:16,680 >> loading configuration file /root/dynamic-dpo-v4/sft-checkpoints/llama-3-8b-base-sft-hh-helpful-4xh200/config.json +[INFO|configuration_utils.py:765] 2026-04-21 23:40:16,680 >> Model config LlamaConfig { + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": 128001, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 8192, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": null, + "rope_theta": 500000.0, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.51.0", + "use_cache": false, + "vocab_size": 128256 +} + +[INFO|modeling_utils.py:1121] 2026-04-21 23:40:16,681 >> loading weights file /root/dynamic-dpo-v4/sft-checkpoints/llama-3-8b-base-sft-hh-helpful-4xh200/model.safetensors.index.json +[INFO|modeling_utils.py:2167] 2026-04-21 23:40:16,681 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16. +[INFO|configuration_utils.py:1142] 2026-04-21 23:40:16,684 >> Generate config GenerationConfig { + "bos_token_id": 128000, + "eos_token_id": 128001, + "use_cache": false +} + + Loading checkpoint shards: 0%| | 0/7 [00:00> All model checkpoint weights were used when initializing LlamaForCausalLM. + +[INFO|modeling_utils.py:4934] 2026-04-21 23:40:27,021 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at /root/dynamic-dpo-v4/sft-checkpoints/llama-3-8b-base-sft-hh-helpful-4xh200. +If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training. +[INFO|configuration_utils.py:1095] 2026-04-21 23:40:27,023 >> loading configuration file /root/dynamic-dpo-v4/sft-checkpoints/llama-3-8b-base-sft-hh-helpful-4xh200/generation_config.json +[INFO|configuration_utils.py:1142] 2026-04-21 23:40:27,023 >> Generate config GenerationConfig { + "bos_token_id": 128000, + "do_sample": true, + "eos_token_id": 128001, + "max_length": 4096, + "temperature": 0.6, + "top_p": 0.9 +} + +[WARNING|trainer.py:821] 2026-04-21 23:40:27,025 >> Trainer.tokenizer is now deprecated. You should use `Trainer.processing_class = processing_class` instead. +[WARNING|trainer.py:816] 2026-04-21 23:40:27,025 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. + Tokenizing train (num_proc=12): 0%| | 0/43598 [00:00> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. + Saving the dataset (0/2 shards): 0%| | 0/43598 [00:00> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. + Tokenizing test (num_proc=12): 0%| | 0/2339 [00:00> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. + Saving the dataset (0/1 shards): 0%| | 0/2339 [00:00> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-22 00:00:45,263 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-22 00:00:45,264 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-22 00:00:45,274 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-22 00:00:45,275 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-22 00:00:45,277 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-22 00:00:45,278 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-22 00:00:45,278 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-22 00:00:45,278 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-22 00:00:45,281 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +/root/dynamic-dpo-v4/scripts/tokenized_dpo_trainer.py:518: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `NewDPOTrainer.__init__`. Use `processing_class` instead. + super().__init__( +[WARNING|trainer.py:816] 2026-04-22 00:00:45,285 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-22 00:00:45,285 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +/root/dynamic-dpo-v4/scripts/tokenized_dpo_trainer.py:518: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `NewDPOTrainer.__init__`. Use `processing_class` instead. + super().__init__( +/root/dynamic-dpo-v4/scripts/tokenized_dpo_trainer.py:518: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `NewDPOTrainer.__init__`. Use `processing_class` instead. + super().__init__( +[INFO|trainer.py:748] 2026-04-22 00:00:45,813 >> Using auto half precision backend +/root/dynamic-dpo-v4/.venv/lib/python3.11/site-packages/accelerate/accelerator.py:1557: UserWarning: Upcasted low precision parameters in LlamaForCausalLM because mixed precision turned on in FSDP. Affects: model.embed_tokens.weight, model.norm.weight, lm_head.weight. + warnings.warn( +/root/dynamic-dpo-v4/.venv/lib/python3.11/site-packages/accelerate/accelerator.py:1557: UserWarning: Upcasted low precision parameters in LlamaDecoderLayer because mixed precision turned on in FSDP. Affects: self_attn.q_proj.weight, self_attn.k_proj.weight, self_attn.v_proj.weight, self_attn.o_proj.weight, mlp.gate_proj.weight, mlp.up_proj.weight, mlp.down_proj.weight, input_layernorm.weight, post_attention_layernorm.weight. + warnings.warn( +/root/dynamic-dpo-v4/.venv/lib/python3.11/site-packages/accelerate/accelerator.py:1563: UserWarning: FSDP upcast of low precision parameters may affect the precision of model checkpoints. + warnings.warn( +[INFO|trainer.py:2414] 2026-04-22 00:00:54,288 >> ***** Running training ***** +[INFO|trainer.py:2415] 2026-04-22 00:00:54,288 >> Num examples = 43,598 +[INFO|trainer.py:2416] 2026-04-22 00:00:54,288 >> Num Epochs = 1 +[INFO|trainer.py:2417] 2026-04-22 00:00:54,288 >> Instantaneous batch size per device = 8 +[INFO|trainer.py:2420] 2026-04-22 00:00:54,288 >> Total train batch size (w. parallel, distributed & accumulation) = 64 +[INFO|trainer.py:2421] 2026-04-22 00:00:54,288 >> Gradient Accumulation steps = 2 +[INFO|trainer.py:2422] 2026-04-22 00:00:54,288 >> Total optimization steps = 681 +[INFO|trainer.py:2423] 2026-04-22 00:00:54,289 >> Number of trainable parameters = 2,007,565,312 +[INFO|integration_utils.py:831] 2026-04-22 00:00:54,289 >> Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true" +wandb: Currently logged in as: feng-cheng (feng-cheng-northeastern-university). Use `wandb login --relogin` to force relogin +wandb: - Waiting for wandb.init()... wandb: \ Waiting for wandb.init()... wandb: wandb version 0.26.0 is available! To upgrade, please run: +wandb: $ pip install wandb --upgrade +wandb: Tracking run with wandb version 0.17.5 +wandb: Run data is saved locally in /root/dynamic-dpo-v4/outputs/wandb/wandb/run-20260422_000055-zcqg1rl4 +wandb: Run `wandb offline` to turn off syncing. +wandb: Syncing run llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802 +wandb: ⭐️ View project at https://wandb.ai/feng-cheng-northeastern-university/llama3-8b-base-new-method-hh-beta-0.1 +wandb: 🚀 View run at https://wandb.ai/feng-cheng-northeastern-university/llama3-8b-base-new-method-hh-beta-0.1/runs/zcqg1rl4 + 0%| | 0/681 [00:00> Could not estimate the number of tokens of the input, floating-point operations will not be computed +[WARNING|modeling_utils.py:1713] 2026-04-22 00:00:58,847 >> Could not estimate the number of tokens of the input, floating-point operations will not be computed +[WARNING|modeling_utils.py:1713] 2026-04-22 00:00:58,847 >> Could not estimate the number of tokens of the input, floating-point operations will not be computed +[WARNING|modeling_utils.py:1713] 2026-04-22 00:00:58,855 >> Could not estimate the number of tokens of the input, floating-point operations will not be computed + 0%|▏ | 1/681 [00:02<31:20, 2.77s/it] {'loss': 1.389, 'grad_norm': 83.52236938476562, 'learning_rate': 0.0, 'fcm_dpo/beta': 0.10000000149011612, 'fcm_dpo/q_t': 0.5005706548690796, 'fcm_dpo/delta': 0.0, 'fcm_dpo/margin': -0.02287006378173828, 'margin_dpo/margin_mean': -0.02287048101425171, 'margin_dpo/margin_std': 0.41920793056488037, 'logps/chosen': -50.1435661315918, 'logps/rejected': -74.09991455078125, 'logps/ref_chosen': -50.14883804321289, 'logps/ref_rejected': -74.1280517578125, 'logits/chosen': -0.4974287748336792, 'logits/rejected': -0.43299180269241333, 'epoch': 0.0} + 0%|▏ | 1/681 [00:02<31:20, 2.77s/it] 0%|▍ | 2/681 [00:05<29:27, 2.60s/it] 0%|▋ | 3/681 [00:07<29:01, 2.57s/it] 1%|▉ | 4/681 [00:10<29:18, 2.60s/it] 1%|█▏ | 5/681 [00:12<29:02, 2.58s/it] {'loss': 1.3898, 'grad_norm': 89.65718078613281, 'learning_rate': 2.898550724637681e-08, 'fcm_dpo/beta': 0.10000000894069672, 'fcm_dpo/q_t': 0.5008123517036438, 'fcm_dpo/delta': 0.0, 'fcm_dpo/margin': -0.03249748796224594, 'margin_dpo/margin_mean': -0.03249724209308624, 'margin_dpo/margin_std': 0.34048572182655334, 'logps/chosen': -56.06619644165039, 'logps/rejected': -78.66961669921875, 'logps/ref_chosen': -56.05734634399414, 'logps/ref_rejected': -78.69325256347656, 'logits/chosen': -0.4900774359703064, 'logits/rejected': -0.453380823135376, 'epoch': 0.01} + 1%|█▏ | 5/681 [00:13<29:02, 2.58s/it] 1%|█▍ | 6/681 [00:15<27:33, 2.45s/it] 1%|█▋ | 7/681 [00:17<26:53, 2.39s/it] 1%|█▉ | 8/681 [00:19<26:37, 2.37s/it] 1%|██▏ | 9/681 [00:22<27:09, 2.42s/it] 1%|██▍ | 10/681 [00:24<27:47, 2.49s/it] {'loss': 1.3888, 'grad_norm': 70.88235473632812, 'learning_rate': 6.521739130434782e-08, 'fcm_dpo/beta': 0.10000000894069672, 'fcm_dpo/q_t': 0.5005213022232056, 'fcm_dpo/delta': 0.0, 'fcm_dpo/margin': 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[07:22<21:14, 2.52s/it] {'loss': 0.8859, 'grad_norm': 37.51227569580078, 'learning_rate': 4.645557588393406e-07, 'fcm_dpo/beta': 0.01827237568795681, 'fcm_dpo/q_t': 0.32835322618484497, 'fcm_dpo/delta': -0.062149811536073685, 'fcm_dpo/margin': 49.67750549316406, 'margin_dpo/margin_mean': 49.67750549316406, 'margin_dpo/margin_std': 58.46363067626953, 'logps/chosen': -106.3938217163086, 'logps/rejected': -186.0583038330078, 'logps/ref_chosen': -62.996971130371094, 'logps/ref_rejected': -92.98394012451172, 'logits/chosen': -0.5422641038894653, 'logits/rejected': -0.5189716815948486, 'epoch': 0.26} + 26%|█████████████████████████████████████████▎ | 175/681 [07:22<21:14, 2.52s/it] 26%|█████████████████████████████████████████▌ | 176/681 [07:24<20:52, 2.48s/it] 26%|█████████████████████████████████████████▊ | 177/681 [07:27<21:06, 2.51s/it] 26%|██████████████████████████████████████████ | 178/681 [07:29<21:13, 2.53s/it] 26%|██████████████████████████████████████████▎ | 179/681 [07:32<21:33, 2.58s/it] 26%|██████████████████████████████████████████▌ | 180/681 [07:34<21:18, 2.55s/it] {'loss': 0.8569, 'grad_norm': 27.392812728881836, 'learning_rate': 4.611919330113591e-07, 'fcm_dpo/beta': 0.01582510769367218, 'fcm_dpo/q_t': 0.31790629029273987, 'fcm_dpo/delta': -0.1366010159254074, 'fcm_dpo/margin': 61.5815315246582, 'margin_dpo/margin_mean': 61.5815315246582, 'margin_dpo/margin_std': 70.3671646118164, 'logps/chosen': -105.83013916015625, 'logps/rejected': -207.4561767578125, 'logps/ref_chosen': -57.0670280456543, 'logps/ref_rejected': -97.1115493774414, 'logits/chosen': -0.485221803188324, 'logits/rejected': -0.48061603307724, 'epoch': 0.26} + 26%|██████████████████████████████████████████▌ | 180/681 [07:35<21:18, 2.55s/it] 27%|██████████████████████████████████████████▊ | 181/681 [07:37<21:18, 2.56s/it] 27%|███████████████████████████████████████████ | 182/681 [07:39<20:58, 2.52s/it] 27%|███████████████████████████████████████████▎ | 183/681 [07:42<20:21, 2.45s/it] 27%|███████████████████████████████████████████▌ | 184/681 [07:44<20:54, 2.52s/it] 27%|███████████████████████████████████████████▋ | 185/681 [07:47<20:36, 2.49s/it] {'loss': 0.9783, 'grad_norm': 36.289039611816406, 'learning_rate': 4.5768898691940836e-07, 'fcm_dpo/beta': 0.015623442828655243, 'fcm_dpo/q_t': 0.3591853976249695, 'fcm_dpo/delta': 0.13391907513141632, 'fcm_dpo/margin': 46.54853057861328, 'margin_dpo/margin_mean': 46.54853057861328, 'margin_dpo/margin_std': 60.472984313964844, 'logps/chosen': -97.86585998535156, 'logps/rejected': -165.0836944580078, 'logps/ref_chosen': -54.840736389160156, 'logps/ref_rejected': -75.51002502441406, 'logits/chosen': -0.5313425064086914, 'logits/rejected': -0.5060660243034363, 'epoch': 0.27} + 27%|███████████████████████████████████████████▋ | 185/681 [07:47<20:36, 2.49s/it] 27%|███████████████████████████████████████████▉ | 186/681 [07:49<20:32, 2.49s/it] 27%|████████████████████████████████████████████▏ | 187/681 [07:52<19:58, 2.43s/it] 28%|████████████████████████████████████████████▍ | 188/681 [07:54<20:10, 2.46s/it] 28%|████████████████████████████████████████████▋ | 189/681 [07:57<20:29, 2.50s/it] 28%|████████████████████████████████████████████▉ | 190/681 [07:59<20:03, 2.45s/it] {'loss': 0.8786, 'grad_norm': 40.74492645263672, 'learning_rate': 4.5404922808905543e-07, 'fcm_dpo/beta': 0.01625588908791542, 'fcm_dpo/q_t': 0.3247375786304474, 'fcm_dpo/delta': -0.08447099477052689, 'fcm_dpo/margin': 56.91190719604492, 'margin_dpo/margin_mean': 56.91190719604492, 'margin_dpo/margin_std': 66.29203033447266, 'logps/chosen': -102.94236755371094, 'logps/rejected': -188.99276733398438, 'logps/ref_chosen': -57.72148895263672, 'logps/ref_rejected': -86.85997009277344, 'logits/chosen': -0.5222185254096985, 'logits/rejected': -0.49877819418907166, 'epoch': 0.28} + 28%|████████████████████████████████████████████▉ | 190/681 [07:59<20:03, 2.45s/it] 28%|█████████████████████████████████████████████▏ | 191/681 [08:02<20:41, 2.53s/it] 28%|█████████████████████████████████████████████▍ | 192/681 [08:04<20:25, 2.51s/it] 28%|█████████████████████████████████████████████▋ | 193/681 [08:07<20:01, 2.46s/it] 28%|█████████████████████████████████████████████▊ | 194/681 [08:09<19:24, 2.39s/it] 29%|██████████████████████████████████████████████ | 195/681 [08:11<19:52, 2.45s/it] {'loss': 0.8883, 'grad_norm': 33.52628707885742, 'learning_rate': 4.5027505416968985e-07, 'fcm_dpo/beta': 0.014897446148097515, 'fcm_dpo/q_t': 0.32934439182281494, 'fcm_dpo/delta': -0.034334540367126465, 'fcm_dpo/margin': 58.907752990722656, 'margin_dpo/margin_mean': 58.907752990722656, 'margin_dpo/margin_std': 67.69029235839844, 'logps/chosen': -110.0854263305664, 'logps/rejected': -200.19639587402344, 'logps/ref_chosen': -58.26164627075195, 'logps/ref_rejected': -89.46485900878906, 'logits/chosen': -0.4740094542503357, 'logits/rejected': -0.4610044062137604, 'epoch': 0.29} + 29%|██████████████████████████████████████████████ | 195/681 [08:12<19:52, 2.45s/it] 29%|██████████████████████████████████████████████▎ | 196/681 [08:14<19:58, 2.47s/it] 29%|██████████████████████████████████████████████▌ | 197/681 [08:17<20:09, 2.50s/it] 29%|██████████████████████████████████████████████▊ | 198/681 [08:19<20:15, 2.52s/it] 29%|███████████████████████████████████████████████ | 199/681 [08:22<20:37, 2.57s/it] 29%|███████████████████████████████████████████████▎ | 200/681 [08:24<20:33, 2.56s/it] {'loss': 0.8697, 'grad_norm': 31.517562866210938, 'learning_rate': 4.4636895135509966e-07, 'fcm_dpo/beta': 0.014423737302422523, 'fcm_dpo/q_t': 0.3252788186073303, 'fcm_dpo/delta': -0.07692910730838776, 'fcm_dpo/margin': 63.713844299316406, 'margin_dpo/margin_mean': 63.713844299316406, 'margin_dpo/margin_std': 73.30004119873047, 'logps/chosen': -105.78775787353516, 'logps/rejected': -196.93441772460938, 'logps/ref_chosen': -55.71953201293945, 'logps/ref_rejected': -83.15235137939453, 'logits/chosen': -0.46841588616371155, 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239/681 [10:42<18:51, 2.56s/it] 35%|████████████████████████████████████████████████████████▋ | 240/681 [10:45<18:46, 2.55s/it] {'loss': 0.8677, 'grad_norm': 40.340721130371094, 'learning_rate': 4.106969024216348e-07, 'fcm_dpo/beta': 0.010825184173882008, 'fcm_dpo/q_t': 0.3274964690208435, 'fcm_dpo/delta': -0.018980540335178375, 'fcm_dpo/margin': 79.97639465332031, 'margin_dpo/margin_mean': 79.97639465332031, 'margin_dpo/margin_std': 84.8770523071289, 'logps/chosen': -138.43865966796875, 'logps/rejected': -254.26034545898438, 'logps/ref_chosen': -55.238983154296875, 'logps/ref_rejected': -91.08428955078125, 'logits/chosen': -0.3556009829044342, 'logits/rejected': -0.34894007444381714, 'epoch': 0.35} + 35%|████████████████████████████████████████████████████████▋ | 240/681 [10:45<18:46, 2.55s/it] 35%|████████████████████████████████████████████████████████▉ | 241/681 [10:47<18:25, 2.51s/it] 36%|█████████████████████████████████████████████████████████▏ | 242/681 [10:50<18:09, 2.48s/it] 36%|█████████████████████████████████████████████████████████▍ | 243/681 [10:52<18:05, 2.48s/it] 36%|█████████████████████████████████████████████████████████▋ | 244/681 [10:55<18:06, 2.49s/it] 36%|█████████████████████████████████████████████████████████▉ | 245/681 [10:57<18:31, 2.55s/it] {'loss': 0.8333, 'grad_norm': 42.48567581176758, 'learning_rate': 4.057290731287531e-07, 'fcm_dpo/beta': 0.010110095143318176, 'fcm_dpo/q_t': 0.3163699507713318, 'fcm_dpo/delta': -0.10127156972885132, 'fcm_dpo/margin': 92.84043884277344, 'margin_dpo/margin_mean': 92.8404312133789, 'margin_dpo/margin_std': 95.6347885131836, 'logps/chosen': -158.22030639648438, 'logps/rejected': -272.03009033203125, 'logps/ref_chosen': -65.08844757080078, 'logps/ref_rejected': -86.05777740478516, 'logits/chosen': -0.35475802421569824, 'logits/rejected': -0.3244848847389221, 'epoch': 0.36} + 36%|█████████████████████████████████████████████████████████▉ | 245/681 [10:58<18:31, 2.55s/it] 36%|██████████████████████████████████████████████████████████▏ | 246/681 [11:00<18:41, 2.58s/it] 36%|██████████████████████████████████████████████████████████▍ | 247/681 [11:03<18:23, 2.54s/it] 36%|██████████████████████████████████████████████████████████▋ | 248/681 [11:05<18:30, 2.56s/it] 37%|██████████████████████████████████████████████████████████▊ | 249/681 [11:08<17:59, 2.50s/it] 37%|███████████████████████████████████████████████████████████ | 250/681 [11:10<17:52, 2.49s/it] {'loss': 0.8678, 'grad_norm': 54.82107162475586, 'learning_rate': 4.006586590948141e-07, 'fcm_dpo/beta': 0.00962417759001255, 'fcm_dpo/q_t': 0.3266645073890686, 'fcm_dpo/delta': -0.020193621516227722, 'fcm_dpo/margin': 90.06238555908203, 'margin_dpo/margin_mean': 90.06238555908203, 'margin_dpo/margin_std': 93.82579040527344, 'logps/chosen': -154.8458709716797, 'logps/rejected': -273.19061279296875, 'logps/ref_chosen': -59.08491897583008, 'logps/ref_rejected': -87.36727142333984, 'logits/chosen': 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39%|██████████████████████████████████████████████████████████████▋ | 265/681 [11:47<17:09, 2.47s/it] {'loss': 0.8593, 'grad_norm': 42.47758865356445, 'learning_rate': 3.848656339557562e-07, 'fcm_dpo/beta': 0.009381098672747612, 'fcm_dpo/q_t': 0.3260762095451355, 'fcm_dpo/delta': -0.015523952431976795, 'fcm_dpo/margin': 92.02482604980469, 'margin_dpo/margin_mean': 92.02482604980469, 'margin_dpo/margin_std': 95.39722442626953, 'logps/chosen': -151.35202026367188, 'logps/rejected': -274.75714111328125, 'logps/ref_chosen': -59.35320281982422, 'logps/ref_rejected': -90.73350524902344, 'logits/chosen': -0.299502432346344, 'logits/rejected': -0.27807632088661194, 'epoch': 0.39} + 39%|██████████████████████████████████████████████████████████████▋ | 265/681 [11:47<17:09, 2.47s/it] 39%|██████████████████████████████████████████████████████████████▉ | 266/681 [11:50<17:03, 2.47s/it] 39%|███████████████████████████████████████████████████████████████ | 267/681 [11:52<17:09, 2.49s/it] 39%|███████████████████████████████████████████████████████████████▎ | 268/681 [11:55<17:07, 2.49s/it] 40%|███████████████████████████████████████████████████████████████▌ | 269/681 [11:57<16:57, 2.47s/it] 40%|███████████████████████████████████████████████████████████████▊ | 270/681 [12:00<17:16, 2.52s/it] {'loss': 0.8569, 'grad_norm': 51.16044616699219, 'learning_rate': 3.794189242333106e-07, 'fcm_dpo/beta': 0.009062977507710457, 'fcm_dpo/q_t': 0.32098034024238586, 'fcm_dpo/delta': -0.06297405064105988, 'fcm_dpo/margin': 100.06938171386719, 'margin_dpo/margin_mean': 100.06938171386719, 'margin_dpo/margin_std': 107.111083984375, 'logps/chosen': -184.3603057861328, 'logps/rejected': -313.5339660644531, 'logps/ref_chosen': -66.30875396728516, 'logps/ref_rejected': -95.4130630493164, 'logits/chosen': -0.23682311177253723, 'logits/rejected': -0.21265558898448944, 'epoch': 0.4} + 40%|███████████████████████████████████████████████████████████████▊ | 270/681 [12:00<17:16, 2.52s/it] 40%|████████████████████████████████████████████████████████████████ | 271/681 [12:02<17:13, 2.52s/it] 40%|████████████████████████████████████████████████████████████████▎ | 272/681 [12:05<17:28, 2.56s/it] 40%|████████████████████████████████████████████████████████████████▌ | 273/681 [12:08<17:29, 2.57s/it] 40%|████████████████████████████████████████████████████████████████▊ | 274/681 [12:10<17:00, 2.51s/it] 40%|█████████████████████████████████████████████████████████████████ | 275/681 [12:13<17:14, 2.55s/it] {'loss': 0.8306, 'grad_norm': 33.570701599121094, 'learning_rate': 3.738869612786737e-07, 'fcm_dpo/beta': 0.008018719032406807, 'fcm_dpo/q_t': 0.3118273615837097, 'fcm_dpo/delta': -0.12382125854492188, 'fcm_dpo/margin': 119.62171936035156, 'margin_dpo/margin_mean': 119.6217269897461, 'margin_dpo/margin_std': 122.45941162109375, 'logps/chosen': -189.1214599609375, 'logps/rejected': -346.2816467285156, 'logps/ref_chosen': -54.69990921020508, 'logps/ref_rejected': 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42%|████████████████████████████████████████████████████████████████████▎ | 289/681 [12:48<16:19, 2.50s/it] 43%|████████████████████████████████████████████████████████████████████▌ | 290/681 [12:50<16:01, 2.46s/it] {'loss': 0.8386, 'grad_norm': 32.6433219909668, 'learning_rate': 3.568162605525952e-07, 'fcm_dpo/beta': 0.006760433316230774, 'fcm_dpo/q_t': 0.3183870315551758, 'fcm_dpo/delta': -0.0626429095864296, 'fcm_dpo/margin': 134.1584930419922, 'margin_dpo/margin_mean': 134.1584930419922, 'margin_dpo/margin_std': 134.99864196777344, 'logps/chosen': -225.1572723388672, 'logps/rejected': -392.53900146484375, 'logps/ref_chosen': -58.37105178833008, 'logps/ref_rejected': -91.59428405761719, 'logits/chosen': -0.07117484509944916, 'logits/rejected': -0.06140874698758125, 'epoch': 0.43} + 43%|████████████████████████████████████████████████████████████████████▌ | 290/681 [12:50<16:01, 2.46s/it] 43%|████████████████████████████████████████████████████████████████████▊ | 291/681 [12:53<16:10, 2.49s/it] 43%|█████████████████████████████████████████████████████████████████████ | 292/681 [12:55<15:52, 2.45s/it] 43%|█████████████████████████████████████████████████████████████████████▎ | 293/681 [12:58<16:08, 2.50s/it] 43%|█████████████████████████████████████████████████████████████████████▌ | 294/681 [13:00<15:52, 2.46s/it] 43%|█████████████████████████████████████████████████████████████████████▋ | 295/681 [13:03<15:43, 2.44s/it] {'loss': 0.8958, 'grad_norm': 46.24834060668945, 'learning_rate': 3.509802512179737e-07, 'fcm_dpo/beta': 0.006551184691488743, 'fcm_dpo/q_t': 0.3340519070625305, 'fcm_dpo/delta': 0.024965018033981323, 'fcm_dpo/margin': 126.14031982421875, 'margin_dpo/margin_mean': 126.14030456542969, 'margin_dpo/margin_std': 140.62646484375, 'logps/chosen': -215.8157958984375, 'logps/rejected': -372.1355285644531, 'logps/ref_chosen': -55.113426208496094, 'logps/ref_rejected': -85.29283905029297, 'logits/chosen': -0.051733147352933884, 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[13:36<16:27, 2.65s/it] 45%|█████████████████████████████████████████████████████████████████████████ | 309/681 [13:39<16:23, 2.64s/it] 46%|█████████████████████████████████████████████████████████████████████████▎ | 310/681 [13:42<16:30, 2.67s/it] {'loss': 0.851, 'grad_norm': 40.4238395690918, 'learning_rate': 3.3308869986991487e-07, 'fcm_dpo/beta': 0.006700504571199417, 'fcm_dpo/q_t': 0.3245096802711487, 'fcm_dpo/delta': -0.0035556063521653414, 'fcm_dpo/margin': 127.2578125, 'margin_dpo/margin_mean': 127.2578125, 'margin_dpo/margin_std': 124.93436431884766, 'logps/chosen': -217.1204071044922, 'logps/rejected': -365.7741394042969, 'logps/ref_chosen': -61.359039306640625, 'logps/ref_rejected': -82.75496673583984, 'logits/chosen': -0.07853604853153229, 'logits/rejected': -0.056885600090026855, 'epoch': 0.46} + 46%|█████████████████████████████████████████████████████████████████████████▎ | 310/681 [13:42<16:30, 2.67s/it] 46%|█████████████████████████████████████████████████████████████████████████▌ | 311/681 [13:44<16:10, 2.62s/it] 46%|█████████████████████████████████████████████████████████████████████████▊ | 312/681 [13:46<15:29, 2.52s/it] 46%|█████████████████████████████████████████████████████████████████████████▉ | 313/681 [13:49<15:02, 2.45s/it] 46%|██████████████████████████████████████████████████████████████████████████▏ | 314/681 [13:51<15:03, 2.46s/it] 46%|██████████████████████████████████████████████████████████████████████████▍ | 315/681 [13:54<15:00, 2.46s/it] {'loss': 0.8915, 'grad_norm': 71.91686248779297, 'learning_rate': 3.270101039870797e-07, 'fcm_dpo/beta': 0.006514403969049454, 'fcm_dpo/q_t': 0.3323710560798645, 'fcm_dpo/delta': 0.00254381587728858, 'fcm_dpo/margin': 129.9599151611328, 'margin_dpo/margin_mean': 129.9599151611328, 'margin_dpo/margin_std': 146.8983612060547, 'logps/chosen': -250.1263885498047, 'logps/rejected': -412.8932189941406, 'logps/ref_chosen': -51.77602005004883, 'logps/ref_rejected': -84.58292388916016, 'logits/chosen': 0.050092458724975586, 'logits/rejected': 0.06273595988750458, 'epoch': 0.46} + 46%|██████████████████████████████████████████████████████████████████████████▍ | 315/681 [13:54<15:00, 2.46s/it] 46%|██████████████████████████████████████████████████████████████████████████▋ | 316/681 [13:56<15:05, 2.48s/it] 47%|██████████████████████████████████████████████████████████████████████████▉ | 317/681 [13:59<15:14, 2.51s/it] 47%|███████████████████████████████████████████████████████████████████████████▏ | 318/681 [14:01<14:59, 2.48s/it] 47%|███████████████████████████████████████████████████████████████████████████▍ | 319/681 [14:04<15:17, 2.54s/it] 47%|███████████████████████████████████████████████████████████████████████████▋ | 320/681 [14:07<15:45, 2.62s/it] {'loss': 0.7846, 'grad_norm': 37.25245666503906, 'learning_rate': 3.208807785813777e-07, 'fcm_dpo/beta': 0.006153360940515995, 'fcm_dpo/q_t': 0.3038443326950073, 'fcm_dpo/delta': -0.12349160015583038, 'fcm_dpo/margin': 156.13824462890625, 'margin_dpo/margin_mean': 156.13824462890625, 'margin_dpo/margin_std': 138.79611206054688, 'logps/chosen': -244.4154815673828, 'logps/rejected': -443.03955078125, 'logps/ref_chosen': -56.777862548828125, 'logps/ref_rejected': -99.26368713378906, 'logits/chosen': -0.020375465974211693, 'logits/rejected': -0.011311917565762997, 'epoch': 0.47} + 47%|███████████████████████████████████████████████████████████████████████████▋ | 320/681 [14:07<15:45, 2.62s/it] 47%|███████████████████████████████████████████████████████████████████████████▉ | 321/681 [14:09<15:23, 2.57s/it] 47%|████████████████████████████████████████████████████████████████████████████▏ | 322/681 [14:12<15:24, 2.57s/it] 47%|████████████████████████████████████████████████████████████████████████████▎ | 323/681 [14:14<15:40, 2.63s/it] 48%|████████████████████████████████████████████████████████████████████████████▌ | 324/681 [14:17<15:11, 2.55s/it] 48%|████████████████████████████████████████████████████████████████████████████▊ | 325/681 [14:20<15:27, 2.60s/it] {'loss': 0.8735, 'grad_norm': 46.0535774230957, 'learning_rate': 3.147047612756302e-07, 'fcm_dpo/beta': 0.00587537931278348, 'fcm_dpo/q_t': 0.3283257484436035, 'fcm_dpo/delta': 0.004868075251579285, 'fcm_dpo/margin': 143.66757202148438, 'margin_dpo/margin_mean': 143.66757202148438, 'margin_dpo/margin_std': 146.94345092773438, 'logps/chosen': -280.6909484863281, 'logps/rejected': -449.8771057128906, 'logps/ref_chosen': -58.28468704223633, 'logps/ref_rejected': -83.80326843261719, 'logits/chosen': 0.04561225324869156, 'logits/rejected': 0.05908801406621933, 'epoch': 0.48} + 48%|████████████████████████████████████████████████████████████████████████████▊ | 325/681 [14:20<15:27, 2.60s/it] 48%|█████████████████████████████████████████████████████████████████████████████ | 326/681 [14:22<15:22, 2.60s/it] 48%|█████████████████████████████████████████████████████████████████████████████▎ | 327/681 [14:25<15:29, 2.63s/it] 48%|█████████████████████████████████████████████████████████████████████████████▌ | 328/681 [14:27<15:22, 2.61s/it] 48%|█████████████████████████████████████████████████████████████████████████████▊ | 329/681 [14:30<15:01, 2.56s/it] 48%|██████████████████████████████████████████████████████████████████████████████ | 330/681 [14:32<15:05, 2.58s/it] {'loss': 0.8302, 'grad_norm': 50.687007904052734, 'learning_rate': 3.084861204504122e-07, 'fcm_dpo/beta': 0.0057464223355054855, 'fcm_dpo/q_t': 0.3170631527900696, 'fcm_dpo/delta': -0.0528077557682991, 'fcm_dpo/margin': 156.2254638671875, 'margin_dpo/margin_mean': 156.2254638671875, 'margin_dpo/margin_std': 149.75978088378906, 'logps/chosen': -278.55731201171875, 'logps/rejected': -466.06658935546875, 'logps/ref_chosen': -62.75822067260742, 'logps/ref_rejected': -94.04203033447266, 'logits/chosen': 0.018803680315613747, 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143.60537719726562, 'margin_dpo/margin_mean': 143.60537719726562, 'margin_dpo/margin_std': 148.41580200195312, 'logps/chosen': -285.34234619140625, 'logps/rejected': -459.047119140625, 'logps/ref_chosen': -58.59650421142578, 'logps/ref_rejected': -88.69586944580078, 'logits/chosen': 0.07965292036533356, 'logits/rejected': 0.0922585278749466, 'epoch': 0.49} + 49%|███████████████████████████████████████████████████████████████████████████████▏ | 335/681 [14:45<14:25, 2.50s/it] 49%|███████████████████████████████████████████████████████████████████████████████▍ | 336/681 [14:48<14:40, 2.55s/it] 49%|███████████████████████████████████████████████████████████████████████████████▋ | 337/681 [14:50<14:10, 2.47s/it] 50%|███████████████████████████████████████████████████████████████████████████████▉ | 338/681 [14:52<14:12, 2.49s/it] 50%|████████████████████████████████████████████████████████████████████████████████▏ | 339/681 [14:55<13:46, 2.42s/it] 50%|████████████████████████████████████████████████████████████████████████████████▍ | 340/681 [14:57<13:57, 2.46s/it] {'loss': 0.825, 'grad_norm': 35.8148193359375, 'learning_rate': 2.959373794541426e-07, 'fcm_dpo/beta': 0.005560899153351784, 'fcm_dpo/q_t': 0.3149014711380005, 'fcm_dpo/delta': -0.058431338518857956, 'fcm_dpo/margin': 162.19033813476562, 'margin_dpo/margin_mean': 162.1903533935547, 'margin_dpo/margin_std': 153.74880981445312, 'logps/chosen': -255.1841278076172, 'logps/rejected': -453.63641357421875, 'logps/ref_chosen': -58.18162155151367, 'logps/ref_rejected': -94.44358825683594, 'logits/chosen': 0.013386556878685951, 'logits/rejected': 0.0291533712297678, 'epoch': 0.5} + 50%|████████████████████████████████████████████████████████████████████████████████▍ | 340/681 [14:57<13:57, 2.46s/it] 50%|████████████████████████████████████████████████████████████████████████████████▌ | 341/681 [14:59<13:32, 2.39s/it] 50%|████████████████████████████████████████████████████████████████████████████████▊ | 342/681 [15:02<14:08, 2.50s/it] 50%|█████████████████████████████████████████████████████████████████████████████████ | 343/681 [15:05<13:45, 2.44s/it] 51%|█████████████████████████████████████████████████████████████████████████████████▎ | 344/681 [15:07<13:51, 2.47s/it] 51%|█████████████████████████████████████████████████████████████████████████████████▌ | 345/681 [15:10<14:07, 2.52s/it] {'loss': 0.8223, 'grad_norm': 50.1009521484375, 'learning_rate': 2.896155456223163e-07, 'fcm_dpo/beta': 0.0053556510247290134, 'fcm_dpo/q_t': 0.31634849309921265, 'fcm_dpo/delta': -0.04881957173347473, 'fcm_dpo/margin': 166.8225860595703, 'margin_dpo/margin_mean': 166.8225860595703, 'margin_dpo/margin_std': 155.08956909179688, 'logps/chosen': -275.6701354980469, 'logps/rejected': -483.6131896972656, 'logps/ref_chosen': -57.9904899597168, 'logps/ref_rejected': -99.11092376708984, 'logits/chosen': -0.0006014652317389846, 'logits/rejected': 0.011017178185284138, 'epoch': 0.51} + 51%|█████████████████████████████████████████████████████████████████████████████████▌ | 345/681 [15:10<14:07, 2.52s/it] 51%|█████████████████████████████████████████████████████████████████████████████████▊ | 346/681 [15:12<14:11, 2.54s/it] 51%|██████████████████████████████████████████████████████████████████████████████████ | 347/681 [15:14<13:29, 2.42s/it] 51%|██████████████████████████████████████████████████████████████████████████████████▎ | 348/681 [15:17<13:25, 2.42s/it] 51%|██████████████████████████████████████████████████████████████████████████████████▌ | 349/681 [15:19<13:38, 2.46s/it] 51%|██████████████████████████████████████████████████████████████████████████████████▋ | 350/681 [15:22<13:51, 2.51s/it] {'loss': 0.8883, 'grad_norm': 61.714805603027344, 'learning_rate': 2.8326761550411346e-07, 'fcm_dpo/beta': 0.005294440779834986, 'fcm_dpo/q_t': 0.3322368264198303, 'fcm_dpo/delta': 0.02646171674132347, 'fcm_dpo/margin': 155.80526733398438, 'margin_dpo/margin_mean': 155.80526733398438, 'margin_dpo/margin_std': 168.00064086914062, 'logps/chosen': -326.2274169921875, 'logps/rejected': -512.9906005859375, 'logps/ref_chosen': -58.29923629760742, 'logps/ref_rejected': -89.25711822509766, 'logits/chosen': 0.13601693511009216, 'logits/rejected': 0.14563313126564026, 'epoch': 0.51} + 51%|██████████████████████████████████████████████████████████████████████████████████▋ | 350/681 [15:22<13:51, 2.51s/it] 52%|██████████████████████████████████████████████████████████████████████████████████▉ | 351/681 [15:25<13:51, 2.52s/it] 52%|███████████████████████████████████████████████████████████████████████████████████▏ | 352/681 [15:27<13:50, 2.52s/it] 52%|███████████████████████████████████████████████████████████████████████████████████▍ | 353/681 [15:30<13:44, 2.51s/it] 52%|███████████████████████████████████████████████████████████████████████████████████▋ | 354/681 [15:32<13:56, 2.56s/it] 52%|███████████████████████████████████████████████████████████████████████████████████▉ | 355/681 [15:35<13:45, 2.53s/it] {'loss': 0.8631, 'grad_norm': 50.927425384521484, 'learning_rate': 2.7689777072570284e-07, 'fcm_dpo/beta': 0.005374093074351549, 'fcm_dpo/q_t': 0.325572669506073, 'fcm_dpo/delta': -0.00456079700961709, 'fcm_dpo/margin': 158.74806213378906, 'margin_dpo/margin_mean': 158.74806213378906, 'margin_dpo/margin_std': 160.3525848388672, 'logps/chosen': -304.60711669921875, 'logps/rejected': -488.50799560546875, 'logps/ref_chosen': -60.788482666015625, 'logps/ref_rejected': -85.94129943847656, 'logits/chosen': 0.049882251769304276, 'logits/rejected': 0.07219198346138, 'epoch': 0.52} + 52%|███████████████████████████████████████████████████████████████████████████████████▉ | 355/681 [15:35<13:45, 2.53s/it] 52%|████████████████████████████████████████████████████████████████████████████████████▏ | 356/681 [15:37<13:58, 2.58s/it] 52%|████████████████████████████████████████████████████████████████████████████████████▍ | 357/681 [15:40<14:24, 2.67s/it] 53%|████████████████████████████████████████████████████████████████████████████████████▋ | 358/681 [15:43<14:40, 2.73s/it] 53%|████████████████████████████████████████████████████████████████████████████████████▊ | 359/681 [15:46<14:20, 2.67s/it] 53%|█████████████████████████████████████████████████████████████████████████████████████ | 360/681 [15:48<13:58, 2.61s/it] {'loss': 0.9029, 'grad_norm': 103.65924835205078, 'learning_rate': 2.7051020734928443e-07, 'fcm_dpo/beta': 0.005639675538986921, 'fcm_dpo/q_t': 0.33998966217041016, 'fcm_dpo/delta': 0.06519472599029541, 'fcm_dpo/margin': 139.63821411132812, 'margin_dpo/margin_mean': 139.63821411132812, 'margin_dpo/margin_std': 152.2101593017578, 'logps/chosen': -303.3082580566406, 'logps/rejected': -465.8846130371094, 'logps/ref_chosen': -57.6871337890625, 'logps/ref_rejected': -80.62527465820312, 'logits/chosen': 0.07553316652774811, 'logits/rejected': 0.0942920446395874, 'epoch': 0.53} + 53%|█████████████████████████████████████████████████████████████████████████████████████ | 360/681 [15:48<13:58, 2.61s/it] 53%|█████████████████████████████████████████████████████████████████████████████████████▎ | 361/681 [15:51<14:02, 2.63s/it] 53%|█████████████████████████████████████████████████████████████████████████████████████▌ | 362/681 [15:53<13:27, 2.53s/it] 53%|█████████████████████████████████████████████████████████████████████████████████████▊ | 363/681 [15:55<12:59, 2.45s/it] 53%|██████████████████████████████████████████████████████████████████████████████████████ | 364/681 [15:58<13:05, 2.48s/it] 54%|██████████████████████████████████████████████████████████████████████████████████████▎ | 365/681 [16:01<13:14, 2.51s/it] {'loss': 0.8274, 'grad_norm': 103.95121002197266, 'learning_rate': 2.641091331089811e-07, 'fcm_dpo/beta': 0.005498864687979221, 'fcm_dpo/q_t': 0.3161132335662842, 'fcm_dpo/delta': -0.058943700045347214, 'fcm_dpo/margin': 164.1685028076172, 'margin_dpo/margin_mean': 164.1685028076172, 'margin_dpo/margin_std': 157.7042236328125, 'logps/chosen': -293.01922607421875, 'logps/rejected': -496.72552490234375, 'logps/ref_chosen': -51.490867614746094, 'logps/ref_rejected': -91.02871704101562, 'logits/chosen': 0.13508400321006775, 'logits/rejected': 0.1376177817583084, 'epoch': 0.54} + 54%|██████████████████████████████████████████████████████████████████████████████████████▎ | 365/681 [16:01<13:14, 2.51s/it] 54%|██████████████████████████████████████████████████████████████████████████████████████▌ | 366/681 [16:03<13:24, 2.56s/it] 54%|██████████████████████████████████████████████████████████████████████████████████████▊ | 367/681 [16:06<13:38, 2.61s/it] 54%|███████████████████████████████████████████████████████████████████████████████████████ | 368/681 [16:08<13:30, 2.59s/it] 54%|███████████████████████████████████████████████████████████████████████████████████████▏ | 369/681 [16:11<14:07, 2.72s/it] 54%|███████████████████████████████████████████████████████████████████████████████████████▍ | 370/681 [16:14<14:04, 2.71s/it] {'loss': 0.8812, 'grad_norm': 51.74324035644531, 'learning_rate': 2.5769876463904263e-07, 'fcm_dpo/beta': 0.005576250609010458, 'fcm_dpo/q_t': 0.3373372554779053, 'fcm_dpo/delta': 0.08467354625463486, 'fcm_dpo/margin': 138.50732421875, 'margin_dpo/margin_mean': 138.50733947753906, 'margin_dpo/margin_std': 134.93521118164062, 'logps/chosen': -298.34576416015625, 'logps/rejected': -468.1741638183594, 'logps/ref_chosen': -58.113502502441406, 'logps/ref_rejected': -89.43451690673828, 'logits/chosen': 0.10848675668239594, 'logits/rejected': 0.11948434263467789, 'epoch': 0.54} + 54%|███████████████████████████████████████████████████████████████████████████████████████▍ | 370/681 [16:14<14:04, 2.71s/it] 54%|███████████████████████████████████████████████████████████████████████████████████████▋ | 371/681 [16:17<13:55, 2.70s/it] 55%|███████████████████████████████████████████████████████████████████████████████████████▉ | 372/681 [16:19<13:11, 2.56s/it] 55%|████████████████████████████████████████████████████████████████████████████████████████▏ | 373/681 [16:22<12:59, 2.53s/it] 55%|████████████████████████████████████████████████████████████████████████████████████████▍ | 374/681 [16:24<13:09, 2.57s/it] 55%|████████████████████████████████████████████████████████████████████████████████████████▋ | 375/681 [16:27<13:07, 2.57s/it] {'loss': 0.8508, 'grad_norm': 49.77079772949219, 'learning_rate': 2.512833246961859e-07, 'fcm_dpo/beta': 0.0056075444445014, 'fcm_dpo/q_t': 0.3194437623023987, 'fcm_dpo/delta': -0.06286319345235825, 'fcm_dpo/margin': 161.43272399902344, 'margin_dpo/margin_mean': 161.43270874023438, 'margin_dpo/margin_std': 164.4888458251953, 'logps/chosen': -326.29534912109375, 'logps/rejected': -511.74200439453125, 'logps/ref_chosen': -65.23600769042969, 'logps/ref_rejected': -89.24995422363281, 'logits/chosen': 0.1206691637635231, 'logits/rejected': 0.14743848145008087, 'epoch': 0.55} + 55%|████████████████████████████████████████████████████████████████████████████████████████▋ | 375/681 [16:27<13:07, 2.57s/it] 55%|████████████████████████████████████████████████████████████████████████████████████████▉ | 376/681 [16:29<13:12, 2.60s/it] 55%|█████████████████████████████████████████████████████████████████████████████████████████▏ | 377/681 [16:32<12:56, 2.55s/it] 56%|█████████████████████████████████████████████████████████████████████████████████████████▎ | 378/681 [16:34<12:14, 2.43s/it] 56%|█████████████████████████████████████████████████████████████████████████████████████████▌ | 379/681 [16:37<12:22, 2.46s/it] 56%|█████████████████████████████████████████████████████████████████████████████████████████▊ | 380/681 [16:39<12:07, 2.42s/it] {'loss': 0.8158, 'grad_norm': 47.95132064819336, 'learning_rate': 2.4486703937790243e-07, 'fcm_dpo/beta': 0.005182487424463034, 'fcm_dpo/q_t': 0.3068377375602722, 'fcm_dpo/delta': -0.11963502317667007, 'fcm_dpo/margin': 185.00277709960938, 'margin_dpo/margin_mean': 185.00277709960938, 'margin_dpo/margin_std': 178.25515747070312, 'logps/chosen': -336.24005126953125, 'logps/rejected': -570.0576171875, 'logps/ref_chosen': -53.33893966674805, 'logps/ref_rejected': -102.15375518798828, 'logits/chosen': 0.1993691623210907, 'logits/rejected': 0.2027546912431717, 'epoch': 0.56} + 56%|█████████████████████████████████████████████████████████████████████████████████████████▊ | 380/681 [16:39<12:07, 2.42s/it] 56%|██████████████████████████████████████████████████████████████████████████████████████████ | 381/681 [16:41<12:18, 2.46s/it] 56%|██████████████████████████████████████████████████████████████████████████████████████████▎ | 382/681 [16:44<12:43, 2.55s/it] 56%|██████████████████████████████████████████████████████████████████████████████████████████▌ | 383/681 [16:47<12:37, 2.54s/it] 56%|██████████████████████████████████████████████████████████████████████████████████████████▊ | 384/681 [16:49<12:34, 2.54s/it] 57%|███████████████████████████████████████████████████████████████████████████████████████████ | 385/681 [16:52<12:23, 2.51s/it] {'loss': 0.8731, 'grad_norm': 34.479740142822266, 'learning_rate': 2.3845413533856514e-07, 'fcm_dpo/beta': 0.0049522449262440205, 'fcm_dpo/q_t': 0.3291183114051819, 'fcm_dpo/delta': 0.019767077639698982, 'fcm_dpo/margin': 167.88037109375, 'margin_dpo/margin_mean': 167.88037109375, 'margin_dpo/margin_std': 172.85272216796875, 'logps/chosen': -353.31121826171875, 'logps/rejected': -552.2758178710938, 'logps/ref_chosen': -58.36262130737305, 'logps/ref_rejected': -89.44685363769531, 'logits/chosen': 0.18332220613956451, 'logits/rejected': 0.19640536606311798, 'epoch': 0.57} + 57%|███████████████████████████████████████████████████████████████████████████████████████████ | 385/681 [16:52<12:23, 2.51s/it] 57%|███████████████████████████████████████████████████████████████████████████████████████████▎ | 386/681 [16:54<12:27, 2.53s/it] 57%|███████████████████████████████████████████████████████████████████████████████████████████▍ | 387/681 [16:57<12:23, 2.53s/it] 57%|███████████████████████████████████████████████████████████████████████████████████████████▋ | 388/681 [16:59<12:00, 2.46s/it] 57%|███████████████████████████████████████████████████████████████████████████████████████████▉ | 389/681 [17:02<11:51, 2.44s/it] 57%|████████████████████████████████████████████████████████████████████████████████████████████▏ | 390/681 [17:04<11:52, 2.45s/it] {'loss': 0.8665, 'grad_norm': 46.603187561035156, 'learning_rate': 2.320488370051681e-07, 'fcm_dpo/beta': 0.004950010217726231, 'fcm_dpo/q_t': 0.32719507813453674, 'fcm_dpo/delta': -0.0016832177061587572, 'fcm_dpo/margin': 171.85813903808594, 'margin_dpo/margin_mean': 171.85813903808594, 'margin_dpo/margin_std': 178.92532348632812, 'logps/chosen': -333.3777160644531, 'logps/rejected': -539.3297119140625, 'logps/ref_chosen': -56.380653381347656, 'logps/ref_rejected': -90.47447204589844, 'logits/chosen': 0.12718243896961212, 'logits/rejected': 0.1411055475473404, 'epoch': 0.57} + 57%|████████████████████████████████████████████████████████████████████████████████████████████▏ | 390/681 [17:04<11:52, 2.45s/it] 57%|████████████████████████████████████████████████████████████████████████████████████████████▍ | 391/681 [17:07<11:59, 2.48s/it] 58%|████████████████████████████████████████████████████████████████████████████████████████████▋ | 392/681 [17:09<12:10, 2.53s/it] 58%|████████████████████████████████████████████████████████████████████████████████████████████▉ | 393/681 [17:12<12:12, 2.54s/it] 58%|█████████████████████████████████████████████████████████████████████████████████████████████▏ | 394/681 [17:14<12:13, 2.56s/it] 58%|█████████████████████████████████████████████████████████████████████████████████████████████▍ | 395/681 [17:17<12:02, 2.52s/it] {'loss': 0.9001, 'grad_norm': 44.36903381347656, 'learning_rate': 2.2565536379453404e-07, 'fcm_dpo/beta': 0.005209635477513075, 'fcm_dpo/q_t': 0.33868879079818726, 'fcm_dpo/delta': 0.042330484837293625, 'fcm_dpo/margin': 155.28634643554688, 'margin_dpo/margin_mean': 155.28634643554688, 'margin_dpo/margin_std': 174.56260681152344, 'logps/chosen': -274.38555908203125, 'logps/rejected': -460.8504943847656, 'logps/ref_chosen': -55.95304489135742, 'logps/ref_rejected': -87.13162994384766, 'logits/chosen': 0.03185752406716347, 'logits/rejected': 0.039340805262327194, 'epoch': 0.58} + 58%|█████████████████████████████████████████████████████████████████████████████████████████████▍ | 395/681 [17:17<12:02, 2.52s/it] 58%|█████████████████████████████████████████████████████████████████████████████████████████████▌ | 396/681 [17:19<11:59, 2.53s/it] 58%|█████████████████████████████████████████████████████████████████████████████████████████████▊ | 397/681 [17:22<11:49, 2.50s/it] 58%|██████████████████████████████████████████████████████████████████████████████████████████████ | 398/681 [17:24<11:30, 2.44s/it] 59%|██████████████████████████████████████████████████████████████████████████████████████████████▎ | 399/681 [17:26<11:13, 2.39s/it] 59%|██████████████████████████████████████████████████████████████████████████████████████████████▌ | 400/681 [17:29<11:18, 2.42s/it] {'loss': 0.8159, 'grad_norm': 40.749473571777344, 'learning_rate': 2.192779273338215e-07, 'fcm_dpo/beta': 0.005051156505942345, 'fcm_dpo/q_t': 0.31105726957321167, 'fcm_dpo/delta': -0.08489851653575897, 'fcm_dpo/margin': 183.75982666015625, 'margin_dpo/margin_mean': 183.7598419189453, 'margin_dpo/margin_std': 173.43460083007812, 'logps/chosen': -283.33929443359375, 'logps/rejected': -499.20843505859375, 'logps/ref_chosen': -64.59160614013672, 'logps/ref_rejected': -96.700927734375, 'logits/chosen': 0.04275448992848396, 'logits/rejected': 0.0653674453496933, 'epoch': 0.59} + 59%|██████████████████████████████████████████████████████████████████████████████████████████████▌ | 400/681 [17:29<11:18, 2.42s/it][INFO|trainer.py:4307] 2026-04-22 00:18:26,598 >> +***** Running Evaluation ***** +[INFO|trainer.py:4309] 2026-04-22 00:18:26,599 >> Num examples = 2339 +[INFO|trainer.py:4312] 2026-04-22 00:18:26,599 >> Batch size = 8 + + 0%| | 0/73 [00:00> +***** Running Evaluation ***** +[INFO|trainer.py:4309] 2026-04-22 00:27:35,703 >> Num examples = 2339 +[INFO|trainer.py:4312] 2026-04-22 00:27:35,703 >> Batch size = 8 + + 0%| | 0/73 [00:00> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + {'train_runtime': 1846.2605, 'train_samples_per_second': 23.614, 'train_steps_per_second': 0.369, 'train_loss': 0.8947697063024523, 'epoch': 1.0} + 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 681/681 [30:43<00:00, 2.53s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 681/681 [30:43<00:00, 2.71s/it] +***** train metrics ***** + epoch = 1.0 + total_flos = 0GF + train_loss = 0.8948 + train_runtime = 0:30:46.26 + train_samples = 43598 + train_samples_per_second = 23.614 + train_steps_per_second = 0.369 +2026-04-22 00:31:40 - INFO - __main__ - *** Training complete *** +2026-04-22 00:31:40 - INFO - __main__ - *** Save model *** +[INFO|configuration_utils.py:419] 2026-04-22 00:32:14,748 >> Configuration saved in /root/dynamic-dpo-v4/outputs/outputs/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802/config.json +[INFO|configuration_utils.py:911] 2026-04-22 00:32:14,750 >> Configuration saved in /root/dynamic-dpo-v4/outputs/outputs/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802/generation_config.json +[INFO|modeling_utils.py:3580] 2026-04-22 00:32:40,444 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 7 checkpoint shards. You can find where each parameters has been saved in the index located at /root/dynamic-dpo-v4/outputs/outputs/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802/model.safetensors.index.json. +[INFO|tokenization_utils_base.py:2510] 2026-04-22 00:32:40,449 >> tokenizer config file saved in /root/dynamic-dpo-v4/outputs/outputs/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802/tokenizer_config.json +[INFO|tokenization_utils_base.py:2519] 2026-04-22 00:32:40,449 >> Special tokens file saved in /root/dynamic-dpo-v4/outputs/outputs/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802/special_tokens_map.json +2026-04-22 00:32:40 - INFO - __main__ - Saved HF-compatible model artifacts to /root/dynamic-dpo-v4/outputs/outputs/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802 +[INFO|modelcard.py:450] 2026-04-22 00:32:43,030 >> Dropping the following result as it does not have all the necessary fields: +{'dataset': {'name': 'Anthropic/hh-rlhf', 'type': 'Anthropic/hh-rlhf'}} +[INFO|configuration_utils.py:419] 2026-04-22 00:32:43,033 >> Configuration saved in /root/dynamic-dpo-v4/outputs/outputs/llama-3-8b-base-new-dpo-hh-helpful-s_star0.85-4xh200-batch-64-20260421-233802/config.json +2026-04-22 00:32:43 - INFO - __main__ - *** Evaluate *** +[INFO|trainer.py:4307] 2026-04-22 00:32:43,035 >> +***** Running Evaluation ***** +[INFO|trainer.py:4309] 2026-04-22 00:32:43,035 >> Num examples = 2339 +[INFO|trainer.py:4312] 2026-04-22 00:32:43,035 >> Batch size = 8 + 0%| | 0/73 [00:00