commit a131fac6aa2d167f06fe41b6700e6aadfc5b11e1 Author: ModelHub XC Date: Fri May 22 21:02:10 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: W-61/llama3-hh-helpful-qt045-b0p3-20260429-085449 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 -text +*.npz filter=lfs diff=lfs 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file mode 100644 index 0000000..b4d39c5 --- /dev/null +++ b/README.md @@ -0,0 +1,62 @@ +--- +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-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449 + results: [] +--- + + + +# llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449 + +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. + +## 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 + + + +### 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..51d2a7a --- /dev/null +++ b/all_results.json @@ -0,0 +1,9 @@ +{ + "epoch": 1.0, + "total_flos": 0.0, + "train_loss": 1.095842420442164, + "train_runtime": 1736.9553, + "train_samples": 43598, + "train_samples_per_second": 25.1, + "train_steps_per_second": 0.392 +} \ 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/generation_config.json b/generation_config.json new file mode 100644 index 0000000..76247c9 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,9 @@ +{ + "bos_token_id": 128000, + 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"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..aa6f46e --- /dev/null +++ b/train.log @@ -0,0 +1,1160 @@ +2026-04-29 16:12:12 - INFO - __main__ - Model parameters ModelArguments(base_model_revision=None, model_name_or_path='/workspace/dynamic-dpo-v4/base_models/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') +2026-04-29 16:12:12 - INFO - __main__ - Data parameters DataArguments(chat_template=None, dataset_mixer={'Anthropic/hh-rlhf': 1.0}, text_column='text', dataset_splits=['train'], dataset_configs=['helpful-base'], dataset_dir=None, preprocessing_num_workers=12, use_persistent_hf_cache=True, hf_cache_dir='/workspace/dynamic-dpo-v4/hf/datasets', truncation_side=None, auto_insert_empty_system_msg=True, disable_thinking=False, preprocessing_log_samples=0, preprocessing_log_dir=None) +2026-04-29 16:12:12 - 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=0.3, +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=False, +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.NO, +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=W-61/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449, +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=/workspace/dynamic-dpo-v4/outputs/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449/runs/Apr29_16-12-11_bc4ce3cd7c4e, +logging_first_step=True, +logging_nan_inf_filter=True, +logging_steps=1, +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=/workspace/dynamic-dpo-v4/outputs/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449/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=/workspace/dynamic-dpo-v4/outputs/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449, +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=False, +push_to_hub=False, +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-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449, +s_star=0.4, +save_hf_model_artifacts=True, +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=/workspace/dynamic-dpo-v4/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-hh-new-dpo-multi-beta-sweep, +warmup_ratio=0.1, +warmup_steps=0, +weight_decay=0.0, +) +2026-04-29 16:12:12 - INFO - __main__ - Using W&B project from training args: llama3-hh-new-dpo-multi-beta-sweep +wandb: Currently logged in as: can-not-fand (can-not-fand-northeastern-university). Use `wandb login --relogin` to force relogin +wandb: wandb version 0.26.1 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 /workspace/dynamic-dpo-v4/wandb/wandb/run-20260429_161214-5ilq5gld +wandb: Run `wandb offline` to turn off syncing. +wandb: Syncing run llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449 +wandb: ⭐️ View project at https://wandb.ai/can-not-fand-northeastern-university/llama3-hh-new-dpo-multi-beta-sweep +wandb: 🚀 View run at https://wandb.ai/can-not-fand-northeastern-university/llama3-hh-new-dpo-multi-beta-sweep/runs/5ilq5gld +2026-04-29 16:12:16 - INFO - __main__ - New-DPO parameters: beta=0.3, q_target=0.45, s_star=0.4, eta=0.1 +2026-04-29 16:12:16 - INFO - __main__ - Using persistent HF datasets cache at /workspace/dynamic-dpo-v4/hf/datasets + Normalizing raw HH preferences (train): 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')`. +/workspace/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] 2026-04-29 16:12:21,516 >> 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. +/workspace/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> Trainer.tokenizer is now deprecated. You should use `Trainer.processing_class = processing_class` instead. +[WARNING|logging.py:328] 2026-04-29 16:12:21,621 >> 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. + Normalizing raw HH preferences (train): 18%|█▊ | 8000/43598 [00:00<00:04, 8346.88 examples/s] Normalizing raw HH preferences (train): 21%|██ | 9150/43598 [00:00<00:03, 9143.55 examples/s] Normalizing raw HH preferences (train): 24%|██▎ | 10317/43598 [00:01<00:03, 9811.78 examples/s] Normalizing raw HH preferences (train): 26%|██▋ | 11479/43598 [00:01<00:03, 10307.45 examples/s] Normalizing raw HH preferences (train): 29%|██▉ | 12655/43598 [00:01<00:02, 10703.82 examples/s] Normalizing raw HH preferences (train): 32%|███▏ | 13820/43598 [00:01<00:02, 10970.92 examples/s] Normalizing raw HH preferences (train): 34%|███▍ | 14978/43598 [00:01<00:02, 11144.66 examples/s] Normalizing raw HH preferences (train): 38%|███▊ | 16678/43598 [00:01<00:02, 11214.61 examples/s] Normalizing raw HH preferences (train): 41%|████ | 17823/43598 [00:01<00:02, 11275.80 examples/s] Normalizing raw HH preferences (train): 45%|████▍ | 19469/43598 [00:01<00:02, 11163.07 examples/s] Normalizing raw HH preferences (train): 47%|████▋ | 20647/43598 [00:01<00:02, 11273.15 examples/s] Normalizing raw HH preferences (train): 50%|█████ | 21800/43598 [00:02<00:01, 11339.49 examples/s] Normalizing raw HH preferences (train): 54%|█████▍ | 23452/43598 [00:02<00:01, 11215.53 examples/s] Normalizing raw HH preferences (train): 57%|█████▋ | 24643/43598 [00:02<00:01, 11316.96 examples/s] Normalizing raw HH preferences (train): 59%|█████▉ | 25787/43598 [00:02<00:01, 11346.30 examples/s] Normalizing raw HH preferences (train): 62%|██████▏ | 26930/43598 [00:02<00:01, 11368.57 examples/s] Normalizing raw HH preferences (train): 66%|██████▌ | 28651/43598 [00:02<00:01, 11304.20 examples/s] Normalizing raw HH preferences (train): 68%|██████▊ | 29829/43598 [00:02<00:01, 11423.49 examples/s] Normalizing raw HH preferences (train): 72%|███████▏ | 31488/43598 [00:02<00:01, 11290.16 examples/s] Normalizing raw HH preferences (train): 75%|███████▍ | 32659/43598 [00:03<00:00, 11392.31 examples/s] Normalizing raw HH preferences (train): 78%|███████▊ | 33809/43598 [00:03<00:00, 11418.77 examples/s] Normalizing raw HH preferences (train): 81%|████████▏ | 35458/43598 [00:03<00:00, 11260.52 examples/s] Normalizing raw HH preferences (train): 84%|████████▍ | 36655/43598 [00:03<00:00, 11342.05 examples/s] Normalizing raw HH preferences (train): 87%|████████▋ | 37805/43598 [00:03<00:00, 11382.60 examples/s] Normalizing raw HH preferences (train): 90%|█████████ | 39451/43598 [00:03<00:00, 11229.91 examples/s] Normalizing raw HH preferences (train): 93%|█████████▎| 40654/43598 [00:03<00:00, 11331.37 examples/s] Normalizing raw HH preferences (train): 96%|█████████▌| 41812/43598 [00:03<00:00, 11394.69 examples/s] Normalizing raw HH preferences (train): 100%|█████████▉| 43473/43598 [00:03<00:00, 11274.15 examples/s] Normalizing raw HH preferences (train): 100%|██████████| 43598/43598 [00:04<00:00, 10860.71 examples/s] +2026-04-29 16:12:24 - INFO - __main__ - Training on the following splits: ['train : 43598'] +[INFO|tokenization_utils_base.py:2058] 2026-04-29 16:12:24,970 >> loading file tokenizer.json +[INFO|tokenization_utils_base.py:2058] 2026-04-29 16:12:24,970 >> loading file tokenizer.model +[INFO|tokenization_utils_base.py:2058] 2026-04-29 16:12:24,970 >> loading file added_tokens.json +[INFO|tokenization_utils_base.py:2058] 2026-04-29 16:12:24,970 >> loading file special_tokens_map.json +[INFO|tokenization_utils_base.py:2058] 2026-04-29 16:12:24,970 >> loading file tokenizer_config.json +[INFO|tokenization_utils_base.py:2058] 2026-04-29 16:12:24,970 >> loading file chat_template.jinja +[INFO|tokenization_utils_base.py:2323] 2026-04-29 16:12:25,361 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +2026-04-29 16:12:25 - 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|> +/workspace/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] 2026-04-29 16:12:25,477 >> loading configuration file /workspace/dynamic-dpo-v4/base_models/llama-3-8b-base-sft-hh-helpful-4xh200/config.json +[INFO|configuration_utils.py:765] 2026-04-29 16:12:25,478 >> 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] 2026-04-29 16:12:25,491 >> loading weights file /workspace/dynamic-dpo-v4/base_models/llama-3-8b-base-sft-hh-helpful-4xh200/model.safetensors.index.json +[INFO|modeling_utils.py:2167] 2026-04-29 16:12:25,492 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16. +[WARNING|logging.py:328] 2026-04-29 16:12:25,495 >> 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] 2026-04-29 16:12:25,496 >> 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] 2026-04-29 16:12:37,323 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at /workspace/dynamic-dpo-v4/base_models/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] 2026-04-29 16:12:37,326 >> loading configuration file /workspace/dynamic-dpo-v4/base_models/llama-3-8b-base-sft-hh-helpful-4xh200/generation_config.json +[INFO|configuration_utils.py:1142] 2026-04-29 16:12:37,327 >> 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] 2026-04-29 16:12:37,329 >> loading configuration file /workspace/dynamic-dpo-v4/base_models/llama-3-8b-base-sft-hh-helpful-4xh200/config.json +[INFO|configuration_utils.py:765] 2026-04-29 16:12:37,329 >> 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] 2026-04-29 16:12:37,331 >> loading weights file /workspace/dynamic-dpo-v4/base_models/llama-3-8b-base-sft-hh-helpful-4xh200/model.safetensors.index.json +[INFO|modeling_utils.py:2167] 2026-04-29 16:12:37,332 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16. +[INFO|configuration_utils.py:1142] 2026-04-29 16:12:37,336 >> 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] 2026-04-29 16:12:48,920 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at /workspace/dynamic-dpo-v4/base_models/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] 2026-04-29 16:12:48,923 >> loading configuration file /workspace/dynamic-dpo-v4/base_models/llama-3-8b-base-sft-hh-helpful-4xh200/generation_config.json +[INFO|configuration_utils.py:1142] 2026-04-29 16:12:48,924 >> 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] 2026-04-29 16:12:48,925 >> Trainer.tokenizer is now deprecated. You should use `Trainer.processing_class = processing_class` instead. +[WARNING|trainer.py:816] 2026-04-29 16:12:48,925 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-29 16:12:48,938 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +/workspace/dynamic-dpo-v4/scripts/tokenized_dpo_trainer.py:522: 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] 2026-04-29 16:12:50,403 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-29 16:12:50,403 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-29 16:12:50,404 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +[WARNING|trainer.py:816] 2026-04-29 16:12:50,427 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +/workspace/dynamic-dpo-v4/scripts/tokenized_dpo_trainer.py:522: 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] 2026-04-29 16:12:50,431 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +/workspace/dynamic-dpo-v4/scripts/tokenized_dpo_trainer.py:522: 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] 2026-04-29 16:12:50,435 >> Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. +/workspace/dynamic-dpo-v4/scripts/tokenized_dpo_trainer.py:522: 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] 2026-04-29 16:12:50,684 >> Using auto half precision backend +/workspace/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( +/workspace/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( +/workspace/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] 2026-04-29 16:12:58,589 >> ***** Running training ***** +[INFO|trainer.py:2415] 2026-04-29 16:12:58,589 >> Num examples = 43,598 +[INFO|trainer.py:2416] 2026-04-29 16:12:58,589 >> Num Epochs = 1 +[INFO|trainer.py:2417] 2026-04-29 16:12:58,589 >> Instantaneous batch size per device = 8 +[INFO|trainer.py:2420] 2026-04-29 16:12:58,589 >> Total train batch size (w. parallel, distributed & accumulation) = 64 +[INFO|trainer.py:2421] 2026-04-29 16:12:58,589 >> Gradient Accumulation steps = 2 +[INFO|trainer.py:2422] 2026-04-29 16:12:58,589 >> Total optimization steps = 681 +[INFO|trainer.py:2423] 2026-04-29 16:12:58,590 >> Number of trainable parameters = 2,007,565,312 +[INFO|integration_utils.py:831] 2026-04-29 16:12:58,591 >> Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true" + 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] 2026-04-29 16:13:00,193 >> Could not estimate the number of tokens of the input, floating-point operations will not be computed +[WARNING|modeling_utils.py:1713] 2026-04-29 16:13:00,202 >> Could not estimate the number of tokens of the input, floating-point operations will not be computed +[WARNING|modeling_utils.py:1713] 2026-04-29 16:13:00,206 >> Could not estimate the number of tokens of the input, floating-point operations will not be computed + 0%| | 1/681 [00:02<31:13, 2.75s/it] {'loss': 1.3971, 'grad_norm': 251.27125549316406, 'learning_rate': 0.0, 'fcm_dpo/beta': 0.30000001192092896, 'fcm_dpo/q_t': 0.501685619354248, '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, 'KL/chosen_KL_mean': 0.00527191162109375, 'KL/rejected_KL_mean': 0.028141021728515625, 'KL/mean': 0.016706019639968872, 'KL/std': 0.272699236869812, 'logits/chosen': -0.4974287748336792, 'logits/rejected': -0.43299180269241333, 'epoch': 0.0} + 0%| | 1/681 [00:02<31:13, 2.75s/it] 0%| | 2/681 [00:05<29:34, 2.61s/it] {'loss': 1.4089, 'grad_norm': 217.6841278076172, 'learning_rate': 7.246376811594203e-09, 'fcm_dpo/beta': 0.30000001192092896, 'fcm_dpo/q_t': 0.5049160718917847, 'fcm_dpo/delta': 0.0, 'fcm_dpo/margin': -0.06572261452674866, 'margin_dpo/margin_mean': 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-68.67259216308594, 'KL/chosen_KL_mean': 0.019153594970703125, 'KL/rejected_KL_mean': -0.02429962158203125, 'KL/mean': -0.0025722086429595947, 'KL/std': 0.2354850471019745, 'logits/chosen': -0.4816104471683502, 'logits/rejected': -0.4421927034854889, 'epoch': 0.0} + 0%| | 3/681 [00:07<29:22, 2.60s/it] 1%| | 4/681 [00:10<29:35, 2.62s/it] {'loss': 1.4047, 'grad_norm': 217.65200805664062, 'learning_rate': 2.1739130434782606e-08, 'fcm_dpo/beta': 0.30000001192092896, 'fcm_dpo/q_t': 0.5037118196487427, 'fcm_dpo/delta': 0.0, 'fcm_dpo/margin': -0.04963979125022888, 'margin_dpo/margin_mean': -0.04964029788970947, 'margin_dpo/margin_std': 0.39235860109329224, 'logps/chosen': -56.76927947998047, 'logps/rejected': -86.59903717041016, 'logps/ref_chosen': -56.7677116394043, 'logps/ref_rejected': -86.64710998535156, 'KL/chosen_KL_mean': -0.001567840576171875, 'KL/rejected_KL_mean': 0.04807281494140625, 'KL/mean': 0.023254141211509705, 'KL/std': 0.26486122608184814, 'logits/chosen': -0.4678453207015991, 'logits/rejected': -0.4402541518211365, 'epoch': 0.01} + 1%| | 4/681 [00:10<29:35, 2.62s/it] 1%| | 5/681 [00:13<29:24, 2.61s/it] {'loss': 1.3902, 'grad_norm': 270.9964294433594, 'learning_rate': 2.898550724637681e-08, 'fcm_dpo/beta': 0.30000001192092896, 'fcm_dpo/q_t': 0.5001676678657532, 'fcm_dpo/delta': 0.0, 'fcm_dpo/margin': -0.00204351544380188, 'margin_dpo/margin_mean': -0.0020435750484466553, 'margin_dpo/margin_std': 0.37501761317253113, 'logps/chosen': -53.82865524291992, 'logps/rejected': -84.11642456054688, 'logps/ref_chosen': -53.859375, 'logps/ref_rejected': -84.14918518066406, 'KL/chosen_KL_mean': 0.030719757080078125, 'KL/rejected_KL_mean': 0.032764434814453125, 'KL/mean': 0.031741127371788025, 'KL/std': 0.2725304961204529, 'logits/chosen': -0.4972953498363495, 'logits/rejected': -0.4523712396621704, 'epoch': 0.01} + 1%| | 5/681 [00:13<29:24, 2.61s/it] 1%| | 6/681 [00:15<27:54, 2.48s/it] {'loss': 1.3894, 'grad_norm': 276.3399963378906, 'learning_rate': 3.6231884057971014e-08, 'fcm_dpo/beta': 0.30000001192092896, 'fcm_dpo/q_t': 0.49989837408065796, 'fcm_dpo/delta': 0.0, 'fcm_dpo/margin': 0.001337289810180664, 'margin_dpo/margin_mean': 0.0013370811939239502, 'margin_dpo/margin_std': 0.3880041539669037, 'logps/chosen': -63.027122497558594, 'logps/rejected': -92.66632080078125, 'logps/ref_chosen': -63.007484436035156, 'logps/ref_rejected': -92.64534759521484, 'KL/chosen_KL_mean': -0.0196380615234375, 'KL/rejected_KL_mean': -0.02097320556640625, 'KL/mean': -0.020306527614593506, 'KL/std': 0.27848026156425476, 'logits/chosen': -0.5145661234855652, 'logits/rejected': -0.47312256693840027, 'epoch': 0.01} + 1%| | 6/681 [00:15<27:54, 2.48s/it] 1%| | 7/681 [00:17<27:22, 2.44s/it] {'loss': 1.3873, 'grad_norm': 245.37692260742188, 'learning_rate': 4.347826086956521e-08, 'fcm_dpo/beta': 0.30000001192092896, 'fcm_dpo/q_t': 0.49938228726387024, 'fcm_dpo/delta': 0.0, 'fcm_dpo/margin': 0.008149892091751099, 'margin_dpo/margin_mean': 0.008150070905685425, 'margin_dpo/margin_std': 0.38711145520210266, 'logps/chosen': -57.74197006225586, 'logps/rejected': -103.8958969116211, 'logps/ref_chosen': -57.774818420410156, 'logps/ref_rejected': -103.92059326171875, 'KL/chosen_KL_mean': 0.03285026550292969, 'KL/rejected_KL_mean': 0.02469635009765625, 'KL/mean': 0.02877350151538849, 'KL/std': 0.30477985739707947, 'logits/chosen': -0.5077540874481201, 'logits/rejected': -0.47386452555656433, 'epoch': 0.01} + 1%| | 7/681 [00:17<27:22, 2.44s/it] 1%| | 8/681 [00:20<27:05, 2.41s/it] {'loss': 1.4116, 'grad_norm': 240.70013427734375, 'learning_rate': 5.0724637681159424e-08, 'fcm_dpo/beta': 0.30000001192092896, 'fcm_dpo/q_t': 0.5049271583557129, 'fcm_dpo/delta': 0.0, 'fcm_dpo/margin': -0.06675639748573303, 'margin_dpo/margin_mean': -0.06675609946250916, 'margin_dpo/margin_std': 0.47787904739379883, 'logps/chosen': -58.718265533447266, 'logps/rejected': -79.24690246582031, 'logps/ref_chosen': -58.716033935546875, 'logps/ref_rejected': -79.3114242553711, 'KL/chosen_KL_mean': -0.0022296905517578125, 'KL/rejected_KL_mean': 0.06452560424804688, 'KL/mean': 0.031146153807640076, 'KL/std': 0.33025887608528137, 'logits/chosen': -0.5015411376953125, 'logits/rejected': -0.47501832246780396, 'epoch': 0.01} + 1%| | 8/681 [00:20<27:05, 2.41s/it] 1%|▏ | 9/681 [00:22<27:37, 2.47s/it] {'loss': 1.3778, 'grad_norm': 254.40870666503906, 'learning_rate': 5.797101449275362e-08, 'fcm_dpo/beta': 0.30000001192092896, 'fcm_dpo/q_t': 0.49667075276374817, 'fcm_dpo/delta': 0.0, 'fcm_dpo/margin': 0.04486778378486633, 'margin_dpo/margin_mean': 0.04486680030822754, 'margin_dpo/margin_std': 0.4566071927547455, 'logps/chosen': -69.84456634521484, 'logps/rejected': -99.625244140625, 'logps/ref_chosen': -69.8668441772461, 'logps/ref_rejected': -99.6026611328125, 'KL/chosen_KL_mean': 0.02227783203125, 'KL/rejected_KL_mean': -0.0225830078125, 'KL/mean': -0.00015251338481903076, 'KL/std': 0.30635231733322144, 'logits/chosen': -0.4882626235485077, 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'KL/rejected_KL_mean': -1170.265625, 'KL/mean': -916.9816284179688, 'KL/std': 600.1426391601562, 'logits/chosen': -1.038741111755371, 'logits/rejected': -1.0553760528564453, 'epoch': 0.99} + 99%|█████████▉| 675/681 [28:41<00:15, 2.54s/it] 99%|█████████▉| 676/681 [28:44<00:13, 2.60s/it] {'loss': 1.0661, 'grad_norm': 27.7912654876709, 'learning_rate': 1.1857007165852472e-10, 'fcm_dpo/beta': 0.0009004472522065043, 'fcm_dpo/q_t': 0.4012463092803955, 'fcm_dpo/delta': -0.03510238975286484, 'fcm_dpo/margin': 481.5036315917969, 'margin_dpo/margin_mean': 481.50360107421875, 'margin_dpo/margin_std': 591.6707763671875, 'logps/chosen': -797.0269165039062, 'logps/rejected': -1295.49072265625, 'logps/ref_chosen': -71.39852905273438, 'logps/ref_rejected': -88.3587646484375, 'KL/chosen_KL_mean': -725.62841796875, 'KL/rejected_KL_mean': -1207.1319580078125, 'KL/mean': -966.3801879882812, 'KL/std': 584.3846435546875, 'logits/chosen': -0.995841920375824, 'logits/rejected': -1.0157501697540283, 'epoch': 0.99} + 99%|█████████▉| 676/681 [28:44<00:13, 2.60s/it] 99%|█████████▉| 677/681 [28:46<00:10, 2.52s/it] {'loss': 1.1046, 'grad_norm': 29.935705184936523, 'learning_rate': 8.23423165278725e-11, 'fcm_dpo/beta': 0.0008935732766985893, 'fcm_dpo/q_t': 0.41068124771118164, 'fcm_dpo/delta': -0.016854500398039818, 'fcm_dpo/margin': 465.65484619140625, 'margin_dpo/margin_mean': 465.65484619140625, 'margin_dpo/margin_std': 735.0831298828125, 'logps/chosen': -786.2052612304688, 'logps/rejected': -1273.559326171875, 'logps/ref_chosen': -56.527435302734375, 'logps/ref_rejected': -78.22654724121094, 'KL/chosen_KL_mean': -729.6778564453125, 'KL/rejected_KL_mean': -1195.332763671875, 'KL/mean': -962.5052490234375, 'KL/std': 609.0902099609375, 'logits/chosen': -1.073415756225586, 'logits/rejected': -1.0710859298706055, 'epoch': 0.99} + 99%|█████████▉| 677/681 [28:46<00:10, 2.52s/it] 100%|█████████▉| 678/681 [28:49<00:07, 2.49s/it] {'loss': 1.0566, 'grad_norm': 33.50616455078125, 'learning_rate': 5.270012410216185e-11, 'fcm_dpo/beta': 0.00088664231589064, 'fcm_dpo/q_t': 0.39224404096603394, 'fcm_dpo/delta': -0.08726058155298233, 'fcm_dpo/margin': 544.7600708007812, 'margin_dpo/margin_mean': 544.7600708007812, 'margin_dpo/margin_std': 738.8284912109375, 'logps/chosen': -654.531982421875, 'logps/rejected': -1233.76220703125, 'logps/ref_chosen': -46.13447570800781, 'logps/ref_rejected': -80.60462951660156, 'KL/chosen_KL_mean': -608.3975830078125, 'KL/rejected_KL_mean': -1153.1575927734375, 'KL/mean': -880.777587890625, 'KL/std': 650.5914306640625, 'logits/chosen': -1.0082026720046997, 'logits/rejected': -1.0482615232467651, 'epoch': 1.0} + 100%|█████████▉| 678/681 [28:49<00:07, 2.49s/it] 100%|█████████▉| 679/681 [28:51<00:05, 2.57s/it] {'loss': 1.1451, 'grad_norm': 31.147796630859375, 'learning_rate': 2.9644275480772416e-11, 'fcm_dpo/beta': 0.0008858998189680278, 'fcm_dpo/q_t': 0.42340487241744995, 'fcm_dpo/delta': 0.06133866682648659, 'fcm_dpo/margin': 384.67840576171875, 'margin_dpo/margin_mean': 384.678466796875, 'margin_dpo/margin_std': 626.0743408203125, 'logps/chosen': -758.1961669921875, 'logps/rejected': -1169.177734375, 'logps/ref_chosen': -50.294921875, 'logps/ref_rejected': -76.59813690185547, 'KL/chosen_KL_mean': -707.9012451171875, 'KL/rejected_KL_mean': -1092.57958984375, 'KL/mean': -900.2404174804688, 'KL/std': 537.857177734375, 'logits/chosen': -1.03668212890625, 'logits/rejected': -1.0291433334350586, 'epoch': 1.0} + 100%|█████████▉| 679/681 [28:51<00:05, 2.57s/it] 100%|█████████▉| 680/681 [28:54<00:02, 2.59s/it] {'loss': 1.0875, 'grad_norm': 36.256160736083984, 'learning_rate': 1.31753782067201e-11, 'fcm_dpo/beta': 0.0008777154725976288, 'fcm_dpo/q_t': 0.39790278673171997, 'fcm_dpo/delta': -0.05377676337957382, 'fcm_dpo/margin': 513.332275390625, 'margin_dpo/margin_mean': 513.332275390625, 'margin_dpo/margin_std': 758.932373046875, 'logps/chosen': -797.5235595703125, 'logps/rejected': -1346.32470703125, 'logps/ref_chosen': -76.91569519042969, 'logps/ref_rejected': -112.384765625, 'KL/chosen_KL_mean': -720.6077880859375, 'KL/rejected_KL_mean': -1233.93994140625, 'KL/mean': -977.27392578125, 'KL/std': 704.15478515625, 'logits/chosen': -1.0335376262664795, 'logits/rejected': -1.0608773231506348, 'epoch': 1.0} + 100%|█████████▉| 680/681 [28:54<00:02, 2.59s/it] 100%|██████████| 681/681 [28:56<00:00, 2.57s/it] {'loss': 1.1404, 'grad_norm': 42.854522705078125, 'learning_rate': 3.2938662507808745e-12, 'fcm_dpo/beta': 0.0008919438696466386, 'fcm_dpo/q_t': 0.41937246918678284, 'fcm_dpo/delta': 0.04472469165921211, 'fcm_dpo/margin': 398.6950988769531, 'margin_dpo/margin_mean': 398.6950988769531, 'margin_dpo/margin_std': 622.65087890625, 'logps/chosen': -768.9052734375, 'logps/rejected': -1195.200927734375, 'logps/ref_chosen': -60.957279205322266, 'logps/ref_rejected': -88.55797576904297, 'KL/chosen_KL_mean': -707.947998046875, 'KL/rejected_KL_mean': -1106.64306640625, 'KL/mean': -907.2955322265625, 'KL/std': 573.4292602539062, 'logits/chosen': -1.0845022201538086, 'logits/rejected': -1.1038618087768555, 'epoch': 1.0} + 100%|██████████| 681/681 [28:56<00:00, 2.57s/it][INFO|trainer.py:2681] 2026-04-29 16:41:55,545 >> + +Training completed. Do not forget to share your model on huggingface.co/models =) + + + {'train_runtime': 1736.9553, 'train_samples_per_second': 25.1, 'train_steps_per_second': 0.392, 'train_loss': 1.095842420442164, 'epoch': 1.0} + 100%|██████████| 681/681 [28:56<00:00, 2.57s/it] 100%|██████████| 681/681 [28:56<00:00, 2.55s/it] +***** train metrics ***** + epoch = 1.0 + total_flos = 0GF + train_loss = 1.0958 + train_runtime = 0:28:56.95 + train_samples = 43598 + train_samples_per_second = 25.1 + train_steps_per_second = 0.392 +2026-04-29 16:41:55 - INFO - __main__ - *** Training complete *** +2026-04-29 16:41:55 - INFO - __main__ - *** Save model *** +[INFO|configuration_utils.py:419] 2026-04-29 16:42:28,395 >> Configuration saved in /workspace/dynamic-dpo-v4/outputs/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449/config.json +[INFO|configuration_utils.py:911] 2026-04-29 16:42:28,399 >> Configuration saved in /workspace/dynamic-dpo-v4/outputs/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449/generation_config.json +[INFO|modeling_utils.py:3580] 2026-04-29 16:43:41,594 >> 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 /workspace/dynamic-dpo-v4/outputs/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449/model.safetensors.index.json. +[INFO|tokenization_utils_base.py:2510] 2026-04-29 16:43:41,599 >> tokenizer config file saved in /workspace/dynamic-dpo-v4/outputs/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449/tokenizer_config.json +[INFO|tokenization_utils_base.py:2519] 2026-04-29 16:43:41,601 >> Special tokens file saved in /workspace/dynamic-dpo-v4/outputs/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449/special_tokens_map.json +2026-04-29 16:43:41 - INFO - __main__ - Saved HF-compatible model artifacts to /workspace/dynamic-dpo-v4/outputs/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449 +[INFO|modelcard.py:450] 2026-04-29 16:43:43,275 >> 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] 2026-04-29 16:43:43,282 >> Configuration saved in /workspace/dynamic-dpo-v4/outputs/llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449/config.json +2026-04-29 16:43:43 - INFO - __main__ - Skipping margin dataset upload because push_margin_dataset is false. +2026-04-29 16:43:43 - INFO - __main__ - *** Training complete! *** +wandb: - 0.011 MB of 0.011 MB uploaded wandb: \ 0.011 MB of 0.011 MB uploaded wandb: | 0.011 MB of 0.635 MB uploaded wandb: / 0.011 MB of 0.635 MB uploaded wandb: - 0.635 MB of 0.635 MB uploaded wandb: \ 0.635 MB of 0.635 MB uploaded wandb: +wandb: Run history: +wandb: train/KL/chosen_KL_mean ████████▇▇▇▇▇▆▆▆▆▅▅▅▅▆▅▅▄▅▄▁▃▃▃▃▃▁▁▂▁▁▁▁ +wandb: train/KL/mean ████████▇▇▇▇▇▆▆▆▆▅▆▆▅▅▅▅▄▅▄▂▃▃▃▃▂▂▂▃▂▁▁▁ +wandb: train/KL/rejected_KL_mean ████████▇▇▇▇▇▇▆▆▆▆▆▆▅▅▅▅▄▅▄▂▃▃▄▃▂▃▃▃▂▁▁▂ +wandb: train/KL/std ▁▁▁▁▁▁▁▂▂▂▂▂▂▃▂▃▃▃▃▃▄▃▃▄▄▄▅▇▆▆▅▆▇▆▆▆▆▇██ +wandb: train/epoch ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███ +wandb: train/fcm_dpo/beta ███▄▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ +wandb: train/fcm_dpo/delta ▆▆▅▂▁▅▄▆▆▅▅▆▆█▅▆▅▆▆▆█▄▇▄▄▅▅▅▇▅▇▄▃█▇▇▅▄▃▅ +wandb: train/fcm_dpo/margin ▁▁▁▁▁▁▁▁▂▂▂▂▂▂▃▃▃▂▃▃▃▄▃▅▄▄▅▅▅▅▄▆▇▄▅▅▆▇█▇ +wandb: train/fcm_dpo/q_t █▇▃▁▁▄▃▄▄▃▃▄▄▅▃▃▃▅▄▄▅▂▄▂▃▃▃▃▄▃▄▃▂▅▄▄▃▃▂▃ +wandb: train/global_step ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███ +wandb: train/grad_norm ██▅▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▂▂▁▁▁▁▂▁▂▂▂▁▂▁▃▂ +wandb: train/learning_rate ▂▃▅▇██████▇▇▇▇▇▆▆▆▆▅▅▅▄▄▄▄▃▃▃▂▂▂▂▂▁▁▁▁▁▁ +wandb: train/logits/chosen ▆▆▆▅▅▅▅▆▇▇██▇▇▇▇█▇█▇▆▆▆▆▆▆▅▂▃▃▃▃▂▁▂▁▁▂▂▁ +wandb: train/logits/rejected ▆▆▆▅▅▅▅▆▇▇██▇█▇▇▇▇█▇▆▆▆▆▆▆▄▂▃▃▃▃▂▁▂▁▁▂▂▁ +wandb: train/logps/chosen ███████▇▇▇▇▇▆▆▆▆▆▅▅▅▅▆▅▅▄▅▄▁▃▃▃▃▃▁▂▂▁▁▁▁ +wandb: train/logps/ref_chosen ▅█▆▆▄▃▆▆▅▄▇▃▄▃█▅▅▄▇▅▃█▇▅▅▃▆▁▇▅▃▇▅▆▇▆▃▆▆▂ +wandb: train/logps/ref_rejected ▇▄█▅▅▃▆█▇▄▅▃▅▆▅▅▄▅█▅█▄▆▃▅▄▃▁▆▃▄▆▃▄█▅▄▂▂▂ +wandb: train/logps/rejected ████████▇▇▇▇▇▇▆▆▆▆▆▆▆▅▅▅▄▅▄▂▃▃▄▃▂▃▃▃▂▁▁▂ +wandb: train/loss █▇▃▂▁▄▃▄▄▄▃▄▄▅▃▃▃▅▃▃▅▂▄▂▂▃▂▄▄▃▄▂▂▅▅▃▃▃▂▃ +wandb: train/margin_dpo/margin_mean ▁▁▁▁▁▁▁▁▂▂▂▂▂▂▃▃▃▂▃▃▃▄▃▅▄▄▅▅▅▅▄▆▇▄▅▅▆▇█▇ +wandb: train/margin_dpo/margin_std ▁▁▁▁▁▁▁▂▂▂▂▂▂▂▂▃▃▃▃▃▄▃▃▄▄▄▄▇▅▆▅▇▇▆█▅▆█▇█ +wandb: +wandb: Run summary: +wandb: total_flos 0.0 +wandb: train/KL/chosen_KL_mean -707.948 +wandb: train/KL/mean -907.29553 +wandb: train/KL/rejected_KL_mean -1106.64307 +wandb: train/KL/std 573.42926 +wandb: train/epoch 1.0 +wandb: train/fcm_dpo/beta 0.00089 +wandb: train/fcm_dpo/delta 0.04472 +wandb: train/fcm_dpo/margin 398.6951 +wandb: train/fcm_dpo/q_t 0.41937 +wandb: train/global_step 681 +wandb: train/grad_norm 42.85452 +wandb: train/learning_rate 0.0 +wandb: train/logits/chosen -1.0845 +wandb: train/logits/rejected -1.10386 +wandb: train/logps/chosen -768.90527 +wandb: train/logps/ref_chosen -60.95728 +wandb: train/logps/ref_rejected -88.55798 +wandb: train/logps/rejected -1195.20093 +wandb: train/loss 1.1404 +wandb: train/margin_dpo/margin_mean 398.6951 +wandb: train/margin_dpo/margin_std 622.65088 +wandb: train_loss 1.09584 +wandb: train_runtime 1736.9553 +wandb: train_samples_per_second 25.1 +wandb: train_steps_per_second 0.392 +wandb: +wandb: 🚀 View run llama-3-8b-base-new-dpo-hh-helpful-4xh200-batch-64-s_star-0.4-eta-0.1-q_t-0.45-beta-0p3-20260429-085449 at: https://wandb.ai/can-not-fand-northeastern-university/llama3-hh-new-dpo-multi-beta-sweep/runs/5ilq5gld +wandb: ⭐️ View project at: https://wandb.ai/can-not-fand-northeastern-university/llama3-hh-new-dpo-multi-beta-sweep +wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s) +wandb: Find logs at: ./wandb/wandb/run-20260429_161214-5ilq5gld/logs +wandb: WARNING The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require("core")`! 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