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--- |
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license: apache-2.0 |
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tags: |
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- alignment-handbook |
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- generated_from_trainer |
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datasets: |
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- HuggingFaceH4/ultrachat_200k |
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base_model: EleutherAI/pythia-1.4b |
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model-index: |
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- name: pythia-1.4b-sft-full |
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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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# pythia-1.4b-sft-full |
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This model is a fine-tuned version of [EleutherAI/pythia-1.4b](https://huggingface.co/EleutherAI/pythia-1.4b) on the HuggingFaceH4/ultrachat_200k dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3818 |
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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: 2e-05 |
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- train_batch_size: 12 |
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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: 96 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.376 | 1.0 | 1335 | 1.3818 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.2.1 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.2 |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nnheui__pythia-1.4b-sft-full) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |34.68| |
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|AI2 Reasoning Challenge (25-Shot)|32.68| |
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|HellaSwag (10-Shot) |52.08| |
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|MMLU (5-Shot) |25.44| |
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|TruthfulQA (0-shot) |38.42| |
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|Winogrande (5-shot) |57.46| |
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|GSM8k (5-shot) | 1.97| |
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