File size: 8,411 Bytes
33597c1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Themis - GGUF
- Model creator: https://huggingface.co/PKU-ONELab/
- Original model: https://huggingface.co/PKU-ONELab/Themis/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Themis.Q2_K.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q2_K.gguf) | Q2_K | 2.96GB |
| [Themis.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Themis.Q3_K.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q3_K.gguf) | Q3_K | 3.74GB |
| [Themis.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Themis.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Themis.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Themis.Q4_0.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Themis.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [Themis.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Themis.Q4_K.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q4_K.gguf) | Q4_K | 4.58GB |
| [Themis.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Themis.Q4_1.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Themis.Q5_0.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Themis.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Themis.Q5_K.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q5_K.gguf) | Q5_K | 5.34GB |
| [Themis.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Themis.Q5_1.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Themis.Q6_K.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q6_K.gguf) | Q6_K | 6.14GB |
| [Themis.Q8_0.gguf](https://huggingface.co/RichardErkhov/PKU-ONELab_-_Themis-gguf/blob/main/Themis.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
license: apache-2.0
---
# Themis
Paper: https://arxiv.org/abs/2406.18365
Github: https://github.com/PKU-ONELab/Themis
## Introduction
We propose **Themis**, an 8B-parameter large language model (LLM) specifically designed and trained for NLG evaluation with more comprehensive capabilities.
Our Themis can evaluate various NLG tasks, including uncommon ones like question-answering evaluation (**Versatility**), in a reference-free manner (**Independence**). Moreover, it allows for specific and customized evaluation aspects and criteria, including overall quality and more fine-grained aspects (**Flexibility**), and its evaluation contains corresponding analysis and explanation together with the rating (**Interpretability**).
We believe that an ideal evaluator should be convenient to use and possess these characteristics. The comparison between related methods and Themis is shown in the table below.
| Method | Versatility | Independence | Flexibility | Interpretability | Open-source |
| :---------------: | :---------: | :----------: | :---------: | :--------------: | :---------: |
| UniEval | ❌ | ❌ | ✔️ | ❌ | ✔️ |
| G-Eval | ✔️ | ✔️ | ✔️ | ✔️ | ❌ |
| X-Eval | ✔️ | ❌ | ✔️ | ❌ | ❌ |
| Prometheus | ✔️ | ❌ | ✔️ | ✔️ | ✔️ |
| Auto-J | ✔️ | ✔️ | ❌ | ✔️ | ✔️ |
| InstructScore | ✔️ | ❌ | ❌ | ✔️ | ✔️ |
| TIGERScore | ✔️ | ✔️ | ❌ | ✔️ | ✔️ |
| **Themis (Ours)** | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
## Performance
We implement experiments on several common NLG evaluation tasks and datasets to compare our Themis with other methods, including SummEval for summarization, Topical-Chat for dialogue response generation, SFRES&SFHOT for data-to-text, QAGS for factuality, MANS for story generation, and WMT23 zh-en for machine translation. Experimental results show that our Themis achieves better overall evaluation performance over other evaluation models, including GPT-4.
| Method | SummEval | Topical-Chat | SFHOT& SFRES | QAGS | MANS | WMT23 | Average Spearman |
| -------------------- | :-------: | :----------: | :---------: | :-------: | :-------: | :-------: | :------------: |
| BLEU | 0.075 | 0.388 | 0.024 | - | 0.032 | 0.021 | - |
| ROUGE | 0.152 | 0.412 | 0.101 | - | -0.002 | 0.151 | - |
| BARTScore | 0.329 | 0.086 | 0.208 | 0.425 | 0.350 | 0.118 | 0.253 |
| BERTScore | 0.231 | 0.394 | 0.139 | - | 0.285 | 0.219 | - |
| BLEURT | 0.152 | 0.388 | 0.244 | - | 0.138 | 0.263 | - |
| CometKiwi | 0.228 | 0.340 | 0.251 | 0.094 | 0.251 | 0.343 | 0.251 |
| UniEval | 0.474 | 0.577 | 0.282 | - | - | - | - |
| G-Eval (GPT-3.5) | 0.409 | 0.585 | - | 0.461 | - | - | - |
| G-Eval (GPT-4) | 0.523 | 0.588 | - | 0.611 | - | - | - |
| GPT-3.5 Turbo | 0.416 | 0.578 | 0.306 | 0.431 | 0.328 | 0.347 | 0.401 |
| GPT-4 Turbo | 0.511 | **0.746** | 0.320 | 0.637 | 0.473 | **0.437** | 0.521 |
| X-Eval | 0.480 | 0.605 | 0.303 | 0.578 | - | - | - |
| Prometheus-13B | 0.163 | 0.434 | 0.173 | - | 0.007 | 0.129 | - |
| Auto-J-13B | 0.198 | 0.425 | 0.141 | 0.226 | 0.380 | 0.104 | 0.246 |
| TIGERScore-13B | 0.384 | 0.346 | 0.200 | 0.504 | 0.231 | 0.248 | 0.319 |
| InstructScore-7B | 0.258 | 0.241 | 0.247 | - | 0.298 | 0.219 | - |
| **Themis-8B (ours)** | **0.553** | 0.725 | **0.333** | **0.684** | **0.551** | 0.405 | **0.542** |
We further conduct more in-depth analyses, including generalization tests on unseen tasks like the instruction-following evaluation as well as aspect-targeted perturbation tests, and our Themis also exhibits superior evaluation performance. For more experimental results and details, please refer to our paper.
## Requirements and Usage
Please refer to our [github repo](https://github.com/PKU-ONELab/Themis) for more details.
## Citation
```
@article{hu2024themis,
title={Themis: Towards Flexible and Interpretable NLG Evaluation},
author={Hu, Xinyu and Lin, Li and Gao, Mingqi and Yin, Xunjian and Wan, Xiaojun},
journal={arXiv preprint arXiv:2406.18365},
year={2024}
}
```
|