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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: llama3.1
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+ base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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+ tags:
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+ - trl
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+ - dpo
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+ - generated_from_trainer
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+ model-index:
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+ - name: lambda-llama-3-8b-ipo-test
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+ results: []
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+ ---
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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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+
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+ # lambda-llama-3-8b-ipo-test
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+
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+ This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8938
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+ - Rewards/chosen: -0.3750
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+ - Rewards/rejected: -0.6076
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+ - Rewards/accuracies: 0.7892
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+ - Rewards/margins: 0.2326
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+ - Logps/rejected: -3.1855
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+ - Logps/chosen: -2.5684
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+ - Logits/rejected: -3.0293
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+ - Logits/chosen: -2.9592
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: Adam with betas=(0.9,0.999) and epsilon=1e-08
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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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+
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+ ### Training results
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+
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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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+ | 1.1749 | 0.1744 | 100 | 1.0763 | -0.1732 | -0.3120 | 0.7892 | 0.1388 | -2.4465 | -2.0638 | -2.5676 | -2.5133 |
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+ | 0.9802 | 0.3489 | 200 | 0.9501 | -0.3184 | -0.5302 | 0.8012 | 0.2118 | -2.9922 | -2.4269 | -2.7873 | -2.7230 |
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+ | 0.9548 | 0.5233 | 300 | 0.9136 | -0.3761 | -0.6028 | 0.8163 | 0.2267 | -3.1736 | -2.5710 | -2.8788 | -2.8087 |
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+ | 0.9834 | 0.6978 | 400 | 0.9041 | -0.3384 | -0.5537 | 0.8042 | 0.2153 | -3.0509 | -2.4770 | -2.9371 | -2.8667 |
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+ | 0.9967 | 0.8722 | 500 | 0.8938 | -0.3750 | -0.6076 | 0.7892 | 0.2326 | -3.1855 | -2.5684 | -3.0293 | -2.9592 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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+ "train_samples": 73350,
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+ "train_samples_per_second": 8.743,
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+ "train_steps_per_second": 0.068
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+ }
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+ "clean_up_tokenization_spaces": true,
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+ "eos_token": "<|eot_id|>",
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+ "model_input_names": [
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+ "input_ids",
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+ "attention_mask"
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+ ],
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+ "model_max_length": 2048,
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+ "pad_token": "<|eot_id|>",
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+ "tokenizer_class": "PreTrainedTokenizerFast"
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+ }
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