初始化项目，由ModelHub XC社区提供模型
Model: garage-bAInd/Platypus-30B Source: Original Platform
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README.md
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---
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language:
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- en
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tags:
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- llama
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license: other
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metrics:
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- MMLU
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- ARC
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- HellaSwag
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- TruthfulQA
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---
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# 🥳 Platypus-30B has arrived!
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Platypus-30B is an instruction fine-tuned model based on the LLaMA-30B transformer architecture.
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## Model Details
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* **Trained by**: Cole Hunter & Ariel Lee
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* **Model type:** **Platypus-30B** is an auto-regressive language model based on the LLaMA transformer architecture.
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* **Language(s)**: English
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* **License for base weights**: License for the base LLaMA model's weights is Meta's [non-commercial bespoke license](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md).
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| Hyperparameter | Value |
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|---------------------------|-------|
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| \\(n_\text{parameters}\\) | 33B |
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| \\(d_\text{model}\\) | 6656 |
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| \\(n_\text{layers}\\) | 60 |
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| \\(n_\text{heads}\\) | 52 |
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## Training Dataset
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Dataset of highly filtered and curated question and answer pairs. Release TBD.
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## Training Procedure
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`garage-bAInd/Platypus-30B` was instruction fine-tuned using LoRA on 4 A100 80GB. For training details and inference instructions please see the [Platypus-30B](https://github.com/arielnlee/Platypus-30B.git) GitHub repo.
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## Reproducing Evaluation Results
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Install LM Evaluation Harness:
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```
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git clone https://github.com/EleutherAI/lm-evaluation-harness
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cd lm-evaluation-harness
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pip install -e .
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```
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Each task was evaluated on a single A100 80GB GPU.
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ARC:
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```
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python main.py --model hf-causal-experimental --model_args pretrained=garage-bAIdnd/Platypus-30B --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/arc_challenge_25shot.json --device cuda --num_fewshot 25
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```
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HellaSwag:
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```
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python main.py --model hf-causal-experimental --model_args pretrained=garage-bAIdnd/Platypus-30B --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/hellaswag_10shot.json --device cuda --num_fewshot 10
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```
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MMLU:
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```
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python main.py --model hf-causal-experimental --model_args pretrained=garage-bAIdnd/Platypus-30B --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/mmlu_5shot.json --device cuda --num_fewshot 5
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```
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TruthfulQA:
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```
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python main.py --model hf-causal-experimental --model_args pretrained=garage-bAIdnd/Platypus-30B --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/Platypus-30B/truthfulqa_0shot.json --device cuda
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```
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## Limitations and bias
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The base LLaMA model is trained on various data, some of which may contain offensive, harmful, and biased content that can lead to toxic behavior. See Section 5.1 of the LLaMA paper. We have not performed any studies to determine how fine-tuning on the aforementioned datasets affect the model's behavior and toxicity. Do not treat chat responses from this model as a substitute for human judgment or as a source of truth. Please use responsibly.
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## Citations
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```bibtex
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@article{touvron2023llama,
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title={LLaMA: Open and Efficient Foundation Language Models},
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author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
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journal={arXiv preprint arXiv:2302.13971},
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year={2023}
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}
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@article{hu2021lora,
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title={LoRA: Low-Rank Adaptation of Large Language Models},
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author={Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Chen, Weizhu},
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journal={CoRR},
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year={2021}
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}
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```
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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_lilloukas__Platypus-30B)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 57.12 |
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| ARC (25-shot) | 64.59 |
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| HellaSwag (10-shot) | 84.24 |
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| MMLU (5-shot) | 64.19 |
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| TruthfulQA (0-shot) | 45.35 |
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| Winogrande (5-shot) | 81.37 |
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| GSM8K (5-shot) | 14.4 |
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| DROP (3-shot) | 45.65 |
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config.json
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"_name_or_path": "./llama30B_hf",
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"model_type": "llama",
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||||
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||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
93400
tokenizer.json
Normal file
93400
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
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||||
size 499723
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||||
33
tokenizer_config.json
Normal file
33
tokenizer_config.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
}
|
||||
Reference in New Issue
Block a user