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Hmehdi515
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phi-3-medium-LoRA

PEFT
Safetensors
trl
sft
Generated from Trainer
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Hmehdi515/lora

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Files changed (8) hide show
  1. README.md +153 -0
  2. adapter_config.json +34 -0
  3. adapter_model.safetensors +3 -0
  4. added_tokens.json +13 -0
  5. special_tokens_map.json +24 -0
  6. tokenizer.model +3 -0
  7. tokenizer_config.json +132 -0
  8. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ base_model: microsoft/Phi-3-medium-4k-instruct
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+ library_name: peft
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+ license: mit
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+ tags:
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+ - trl
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+ - sft
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+ - generated_from_trainer
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+ model-index:
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+ - name: phi-3-medium-LoRA
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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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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/hmehdi-endosoft/Phi3-medium-ft-python-code/runs/lshnwbbw)
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+ # phi-3-medium-LoRA
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+
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+ This model is a fine-tuned version of [microsoft/Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7006
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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: 0.0001
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 4
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:------:|:---------------:|
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+ | 1.2073 | 0.1118 | 2500 | 0.7942 |
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+ | 1.1026 | 0.2237 | 5000 | 0.7623 |
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+ | 1.0828 | 0.3355 | 7500 | 0.7447 |
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+ | 1.0777 | 0.4473 | 10000 | 0.7363 |
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+ | 1.0761 | 0.5592 | 12500 | 0.7304 |
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+ | 1.0603 | 0.6710 | 15000 | 0.7243 |
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+ | 1.0689 | 0.7828 | 17500 | 0.7208 |
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+ | 1.0685 | 0.8947 | 20000 | 0.7183 |
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+ | 1.0543 | 1.0065 | 22500 | 0.7158 |
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+ | 1.0412 | 1.1183 | 25000 | 0.7126 |
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+ | 1.0488 | 1.2301 | 27500 | 0.7109 |
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+ | 1.0496 | 1.3420 | 30000 | 0.7101 |
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+ | 1.0442 | 1.4538 | 32500 | 0.7088 |
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+ | 1.0533 | 1.5656 | 35000 | 0.7079 |
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+ | 1.0461 | 1.6775 | 37500 | 0.7060 |
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+ | 1.0387 | 1.7893 | 40000 | 0.7059 |
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+ | 1.0214 | 1.9011 | 42500 | 0.7045 |
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+ | 1.0396 | 2.0130 | 45000 | 0.7053 |
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+ | 1.0423 | 2.1248 | 47500 | 0.7044 |
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+ | 1.0384 | 2.2366 | 50000 | 0.7039 |
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+ | 1.0091 | 2.3485 | 52500 | 0.7041 |
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+ | 1.0277 | 2.4603 | 55000 | 0.7040 |
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+ | 1.0194 | 2.5721 | 57500 | 0.7039 |
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+ | 1.0365 | 2.6840 | 60000 | 0.7034 |
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+ | 1.0378 | 2.7958 | 62500 | 0.7021 |
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+ | 1.0315 | 2.9076 | 65000 | 0.7025 |
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+ | 1.0308 | 3.0195 | 67500 | 0.7021 |
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+ | 1.0054 | 3.1313 | 70000 | 0.7022 |
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+ | 1.0275 | 3.2431 | 72500 | 0.7027 |
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+ | 1.024 | 3.3550 | 75000 | 0.7030 |
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+ | 1.0199 | 3.4668 | 77500 | 0.7018 |
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+ | 1.028 | 3.5786 | 80000 | 0.7021 |
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+ | 1.0292 | 3.6904 | 82500 | 0.7017 |
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+ | 1.017 | 3.8023 | 85000 | 0.7014 |
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+ | 1.0202 | 3.9141 | 87500 | 0.7012 |
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+ | 1.01 | 4.0259 | 90000 | 0.7021 |
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+ | 1.0117 | 4.1378 | 92500 | 0.7015 |
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+ | 1.0078 | 4.2496 | 95000 | 0.7010 |
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+ | 1.0181 | 4.3614 | 97500 | 0.7013 |
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+ | 1.0158 | 4.4733 | 100000 | 0.7015 |
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+ | 1.0185 | 4.5851 | 102500 | 0.7015 |
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+ | 1.0145 | 4.6969 | 105000 | 0.7010 |
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+ | 1.006 | 4.8088 | 107500 | 0.7010 |
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+ | 1.0099 | 4.9206 | 110000 | 0.7008 |
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+ | 1.0273 | 5.0324 | 112500 | 0.7010 |
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+ | 1.0081 | 5.1443 | 115000 | 0.7012 |
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+ | 1.0084 | 5.2561 | 117500 | 0.7011 |
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+ | 1.0088 | 5.3679 | 120000 | 0.7012 |
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+ | 1.0021 | 5.4798 | 122500 | 0.7009 |
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+ | 1.0211 | 5.5916 | 125000 | 0.7009 |
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+ | 1.023 | 5.7034 | 127500 | 0.7006 |
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+ | 1.0143 | 5.8153 | 130000 | 0.7008 |
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+ | 1.0082 | 5.9271 | 132500 | 0.7007 |
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+ | 1.0142 | 6.0389 | 135000 | 0.7009 |
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+ | 1.0221 | 6.1507 | 137500 | 0.7009 |
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+ | 1.0245 | 6.2626 | 140000 | 0.7009 |
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+ | 1.0134 | 6.3744 | 142500 | 0.7008 |
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+ | 1.0098 | 6.4862 | 145000 | 0.7007 |
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+ | 1.0123 | 6.5981 | 147500 | 0.7005 |
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+ | 1.0016 | 6.7099 | 150000 | 0.7005 |
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+ | 1.0123 | 6.8217 | 152500 | 0.7006 |
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+ | 1.0085 | 6.9336 | 155000 | 0.7005 |
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+ | 1.0138 | 7.0454 | 157500 | 0.7003 |
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+ | 1.006 | 7.1572 | 160000 | 0.7006 |
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+ | 1.0087 | 7.2691 | 162500 | 0.7005 |
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+ | 1.0152 | 7.3809 | 165000 | 0.7008 |
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+ | 1.0129 | 7.4927 | 167500 | 0.7008 |
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+ | 0.992 | 7.6046 | 170000 | 0.7001 |
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+ | 0.9972 | 7.7164 | 172500 | 0.7004 |
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+ | 1.0168 | 7.8282 | 175000 | 0.7007 |
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+ | 1.0053 | 7.9401 | 177500 | 0.7005 |
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+ | 0.9945 | 8.0519 | 180000 | 0.7004 |
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+ | 1.0186 | 8.1637 | 182500 | 0.7006 |
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+ | 1.0209 | 8.2756 | 185000 | 0.7006 |
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+ | 1.013 | 8.3874 | 187500 | 0.7006 |
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+ | 1.0068 | 8.4992 | 190000 | 0.7006 |
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+ | 0.9985 | 8.6110 | 192500 | 0.7005 |
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+ | 1.0044 | 8.7229 | 195000 | 0.7005 |
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+ | 1.0292 | 8.8347 | 197500 | 0.7005 |
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+ | 1.0153 | 8.9465 | 200000 | 0.7004 |
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+ | 1.0058 | 9.0584 | 202500 | 0.7005 |
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+ | 0.9943 | 9.1702 | 205000 | 0.7005 |
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+ | 1.015 | 9.2820 | 207500 | 0.7005 |
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+ | 1.023 | 9.3939 | 210000 | 0.7006 |
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+ | 1.0192 | 9.5057 | 212500 | 0.7005 |
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+ | 1.0067 | 9.6175 | 215000 | 0.7005 |
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+ | 1.0198 | 9.7294 | 217500 | 0.7006 |
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+ | 0.988 | 9.8412 | 220000 | 0.7006 |
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+ | 1.0098 | 9.9530 | 222500 | 0.7006 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.11.1
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+ - Transformers 4.42.4
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+ - Pytorch 2.3.1+cu121
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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