Model save

README.md

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+---
+license: other
+library_name: peft
+tags:
+- trl
+- sft
+- generated_from_trainer
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+datasets:
+- generator
+metrics:
+- bleu
+- rouge
+model-index:
+- name: Meta-Llama-3-8B-Instruct-advisegpt-v0.2
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Meta-Llama-3-8B-Instruct-advisegpt-v0.2
+
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6891
+- Bleu: {'bleu': 0.7794801643070653, 'precisions': [0.8826931860836374, 0.7921738670614986, 0.7521498106470706, 0.7302911239298923], 'brevity_penalty': 0.9901418189906349, 'length_ratio': 0.9901900930687305, 'translation_length': 663363, 'reference_length': 669935}
+- Rouge: {'rouge1': 0.8797610930416109, 'rouge2': 0.7838158722398209, 'rougeL': 0.8517529678496154, 'rougeLsum': 0.8731754875691802}
+- Exact Match: {'exact_match': 0.0}
+
+## 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: 2e-05
+- train_batch_size: 5
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 12
+- total_train_batch_size: 60
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Bleu                                                                                                                                                                                                                                                              | Rouge                                                                                                                       | Exact Match          |
+|:-------------:|:------:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------:|:--------------------:|
+| 0.1221        | 0.9967 | 175  | 0.6891          | {'bleu': 0.7794801643070653, 'precisions': [0.8826931860836374, 0.7921738670614986, 0.7521498106470706, 0.7302911239298923], 'brevity_penalty': 0.9901418189906349, 'length_ratio': 0.9901900930687305, 'translation_length': 663363, 'reference_length': 669935} | {'rouge1': 0.8797610930416109, 'rouge2': 0.7838158722398209, 'rougeL': 0.8517529678496154, 'rougeLsum': 0.8731754875691802} | {'exact_match': 0.0} |
+| 0.1091        | 1.9991 | 351  | 0.6977          | {'bleu': 0.7805322713844085, 'precisions': [0.8833412231532545, 0.7931277801953389, 0.7535080094374768, 0.7317717661200727], 'brevity_penalty': 0.9900498636013274, 'length_ratio': 0.990099039459052, 'translation_length': 663302, 'reference_length': 669935}  | {'rouge1': 0.88033924999596, 'rouge2': 0.7849601251129642, 'rougeL': 0.8519921287058778, 'rougeLsum': 0.8736913571890462}   | {'exact_match': 0.0} |
+| 0.1067        | 2.9900 | 525  | 0.7051          | {'bleu': 0.7808878497559923, 'precisions': [0.8838378429742967, 0.7938818670645449, 0.7542948740286441, 0.7326395901316979], 'brevity_penalty': 0.9895748787367024, 'length_ratio': 0.9896288445894005, 'translation_length': 662987, 'reference_length': 669935} | {'rouge1': 0.8806020535666979, 'rouge2': 0.7857024053578856, 'rougeL': 0.8520805662216797, 'rougeLsum': 0.8739154999822791} | {'exact_match': 0.0} |
+
+
+### Framework versions
+
+- PEFT 0.10.0
+- Transformers 4.40.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

adapter_model.safetensors

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 size 2806378968

results.json

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+Pre-training results: 
+{"eval_loss": 2.3326611518859863, "eval_bleu": {"bleu": 0.5454691474666797, "precisions": [0.7673002426556667, 0.55498965343115, 0.47555838550638013, 0.4399945816996907], "brevity_penalty": 0.9983776325955439, "length_ratio": 0.9983789472112966, "translation_length": 668849, "reference_length": 669935}, "eval_rouge": {"rouge1": 0.7711607888144418, "rouge2": 0.5464636265187319, "rougeL": 0.6786521367857117, "rougeLsum": 0.7626278756724272}, "eval_exact_match": {"exact_match": 0.0}, "eval_runtime": 455.9955, "eval_samples_per_second": 3.241, "eval_steps_per_second": 0.811}
+ Post-training results: 
+{"eval_loss": 0.689052164554596, "eval_bleu": {"bleu": 0.7794801643070653, "precisions": [0.8826931860836374, 0.7921738670614986, 0.7521498106470706, 0.7302911239298923], "brevity_penalty": 0.9901418189906349, "length_ratio": 0.9901900930687305, "translation_length": 663363, "reference_length": 669935}, "eval_rouge": {"rouge1": 0.8797610930416109, "rouge2": 0.7838158722398209, "rougeL": 0.8517529678496154, "rougeLsum": 0.8731754875691802}, "eval_exact_match": {"exact_match": 0.0}, "eval_runtime": 454.9212, "eval_samples_per_second": 3.249, "eval_steps_per_second": 0.813, "epoch": 2.990033222591362}