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Model: jackf857/llama-3-8b-base-ipo-ultrafeedback-8xh200 Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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base_model: W-61/llama-3-8b-base-sft-ultrachat-8xh200
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tags:
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- alignment-handbook
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- ipo
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- generated_from_trainer
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datasets:
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- HuggingFaceH4/ultrafeedback_binarized
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model-index:
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- name: llama-3-8b-base-ipo-ultrafeedback-8xh200
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# llama-3-8b-base-ipo-ultrafeedback-8xh200
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This model is a fine-tuned version of [W-61/llama-3-8b-base-sft-ultrachat-8xh200](https://huggingface.co/W-61/llama-3-8b-base-sft-ultrachat-8xh200) on the HuggingFaceH4/ultrafeedback_binarized dataset.
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It achieves the following results on the evaluation set:
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- Loss: 20.1926
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- Rewards/chosen: -0.1172
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- Rewards/rejected: -0.2002
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- Rewards/accuracies: 0.7621
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- Rewards/margins: 0.0830
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- Logps/rejected: -3.3356
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- Logps/chosen: -2.3107
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- Logits/rejected: -0.6593
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- Logits/chosen: -0.6771
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-07
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- total_eval_batch_size: 32
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
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|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
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| 87.5502 | 0.4188 | 200 | 21.8803 | -0.0519 | -0.1012 | 0.7056 | 0.0493 | -2.3452 | -1.6582 | -0.7170 | -0.7410 |
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| 80.9952 | 0.8377 | 400 | 20.1926 | -0.1172 | -0.2002 | 0.7621 | 0.0830 | -3.3356 | -2.3107 | -0.6593 | -0.6771 |
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### Framework versions
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- Transformers 4.51.0
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- Pytorch 2.3.1+cu121
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- Datasets 2.21.0
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- Tokenizers 0.21.4
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all_results.json
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{
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"epoch": 0.9989528795811519,
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"eval_logits/chosen": -0.6620355248451233,
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"eval_logits/rejected": -0.6459746956825256,
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"eval_logps/chosen": -2.3574233055114746,
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"eval_logps/rejected": -3.391777753829956,
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"eval_loss": 20.139907836914062,
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"eval_rewards/accuracies": 0.7580645084381104,
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"eval_rewards/chosen": -0.12184108048677444,
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"eval_rewards/margins": 0.0839909017086029,
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"eval_rewards/rejected": -0.20583198964595795,
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"eval_runtime": 44.1837,
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"eval_samples": 2000,
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"eval_samples_per_second": 45.266,
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"eval_steps_per_second": 1.426,
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"total_flos": 0.0,
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"train_loss": 87.11727690946631,
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"train_runtime": 3396.1479,
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"train_samples": 61135,
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"train_samples_per_second": 18.001,
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"train_steps_per_second": 0.14
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}
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config.json
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"architectures": [
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"LlamaForCausalLM"
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],
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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eval_results.json
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"eval_loss": 20.139907836914062,
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"eval_rewards/accuracies": 0.7580645084381104,
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"eval_rewards/chosen": -0.12184108048677444,
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"eval_rewards/margins": 0.0839909017086029,
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"eval_runtime": 44.1837,
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"eval_samples": 2000,
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"eval_samples_per_second": 45.266,
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"eval_steps_per_second": 1.426
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Reference in New Issue
Block a user