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Model: argilla/zephyr-7b-spin-iter3-v0 Source: Original Platform
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license: apache-2.0
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base_model: argilla/zephyr-7b-spin-iter2-v0
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tags:
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- generated_from_trainer
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model-index:
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- name: zephyr-7b-spin-iter3-v0
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results: []
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datasets:
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- argilla/10k_prompts_SPIN_iter3_zephyr_top
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- argilla/10k_prompts_SPIN_iter2_zephyr_top
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- DIBT/10k_prompts_ranked
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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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# zephyr-7b-spin-iter3-v0
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> A model matching the results of SPIN with very little data (30x less), carefully curated by the amazing [Data Is Better Together community](https://huggingface.co/DIBT)
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<div>
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<img src="https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/aEzpD6gvn0xOrN2rNzpZI.webp">
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</div>
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<p align="center">
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<a href="https://github.com/argilla-io/distilabel">
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<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
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</a>
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</p>
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This model is a fine-tuned version of [argilla/zephyr-7b-spin-iter2-v0](https://huggingface.co/argilla/zephyr-7b-spin-iter2-v0) on the
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[argilla/10k_prompts_SPIN_iter3_zephyr_top](https://huggingface.co/datasets/argilla/10k_prompts_SPIN_iter3_zephyr_top) and the
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[argilla/10k_prompts_SPIN_iter2_zephyr_top](https://huggingface.co/datasets/argilla/10k_prompts_SPIN_iter2_zephyr_top) dataset.
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Check [this repo](https://github.com/argilla-io/distilabel-spin-dibt) for full reproducible code using the original SPIN implementation and distilabel.
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If you want to contribute to high quality datasets like this, contribute to the [DIBT prompt collective initiative](https://huggingface.co/spaces/DIBT/prompt-collective-dashboard).
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## MT-Bench results
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| Model | 1st Turn Score | 2nd Turn Score | Average Score | SPIN paper Score |
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|-------------------------|----------------|----------------|---------------|------------------|
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| zephyr-7b-sft-full | 6.6625 | 6.0250 | 6.34375 | 5.94 |
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| zephyr-7b-spin-iter0-v0 | 6.64375 | 6.1750 | 6.409375 | 6.46 |
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| zephyr-7b-spin-iter1-v0 | 6.90625 | 6.3000 | 6.603125 | 6.65 |
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| zephyr-7b-spin-iter2-v0 | **7.1375** | 6.3125 | 6.725000 | 6.78 |
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| zephyr-7b-spin-iter3-v0 | 7.09375 | **6.4500** | **6.771875** | - |
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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: 1e-07
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 64
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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: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 2.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rewards/real | Rewards/generated | Rewards/accuracies | Rewards/margins | Logps/generated | Logps/real | Logits/generated | Logits/real |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------------:|:------------------:|:---------------:|:---------------:|:----------:|:----------------:|:-----------:|
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| 0.2928 | 0.49 | 25 | 0.3951 | -2.6212 | -20.3268 | 0.9062 | 17.7056 | -700.5638 | -278.0876 | -2.8098 | -2.8090 |
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| 0.1487 | 0.97 | 50 | 0.1319 | -2.9077 | -29.1459 | 0.9375 | 26.2382 | -702.3276 | -278.1449 | -2.8218 | -2.8066 |
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| 0.006 | 1.46 | 75 | 0.1269 | -2.6037 | -29.1519 | 0.9583 | 26.5482 | -702.3289 | -278.0841 | -2.8175 | -2.8037 |
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| 0.0086 | 1.94 | 100 | 0.1099 | -2.9181 | -29.6970 | 0.9271 | 26.7789 | -702.4378 | -278.1470 | -2.8177 | -2.8051 |
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### Framework versions
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- Transformers 4.37.0
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- Pytorch 2.1.2+cu121
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- Datasets 2.14.6
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- Tokenizers 0.15.2
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