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import torch
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
device = 'cuda'
model_name = 'robinsmits/Qwen1.5-7B-Dutch-Chat-Dpo'
model = AutoPeftModelForCausalLM.from_pretrained(model_name,
device_map = "auto",
load_in_4bit = True,
torch_dtype = torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_name)
messages = [{"role": "user", "content": "Hoi hoe gaat het ermee? Wat kun je me vertellen over appels?"}]
encoded_ids = tokenizer.apply_chat_template(messages,
add_generation_prompt = True,
return_tensors = "pt")
generated_ids = model.generate(input_ids = encoded_ids.to(device),
max_new_tokens = 256,
do_sample = True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])<|im_start|>system
Je bent een behulpzame AI assistent<|im_end|>
<|im_start|>user
Hoi hoe gaat het ermee? Wat kun je me vertellen over appels?<|im_end|>
<|im_start|>assistant
Hallo! Appels zijn zoet, knapperig en hebben een mooie smaak. Ze zijn groen met roze tinten en er zijn verschillende soorten zoals de Granny Smith, Red Delicious en Gala. Er wordt gezegd dat appels goed zijn voor je gezondheid omdat ze veel vezels en vitamines bevatten. Ook kunnen ze lekker worden gegeten zonder te bakken of te koken. Er zijn ook veel verschillende dingen die je met appels kunt doen zoals het maken van appeltaart of het drinken van appelcider.<|im_end|>| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5503 | 0.1 | 30 | 0.4684 | -0.0439 | -0.6295 | 0.8919 | 0.5856 | -837.9513 | -769.8103 | -0.9335 | -0.8894 |
| 0.4178 | 0.2 | 60 | 0.3568 | -0.3713 | -1.4769 | 0.9015 | 1.1056 | -854.9000 | -776.3594 | -0.8768 | -0.8276 |
| 0.3264 | 0.29 | 90 | 0.3143 | -0.4893 | -1.8730 | 0.9151 | 1.3837 | -862.8228 | -778.7191 | -0.8428 | -0.7929 |
| 0.2999 | 0.39 | 120 | 0.2885 | -0.6832 | -2.3118 | 0.9151 | 1.6286 | -871.5981 | -782.5971 | -0.8260 | -0.7730 |
| 0.3454 | 0.49 | 150 | 0.2749 | -0.7239 | -2.4904 | 0.9189 | 1.7664 | -875.1693 | -783.4113 | -0.8235 | -0.7678 |
| 0.3354 | 0.59 | 180 | 0.2685 | -0.6775 | -2.4859 | 0.9170 | 1.8084 | -875.0795 | -782.4824 | -0.8130 | -0.7574 |
| 0.2848 | 0.68 | 210 | 0.2652 | -0.7157 | -2.5692 | 0.9131 | 1.8535 | -876.7465 | -783.2466 | -0.8157 | -0.7586 |
| 0.3437 | 0.78 | 240 | 0.2621 | -0.7233 | -2.6091 | 0.9151 | 1.8857 | -877.5430 | -783.3994 | -0.8138 | -0.7561 |
| 0.2655 | 0.88 | 270 | 0.2611 | -0.7183 | -2.6154 | 0.9151 | 1.8971 | -877.6708 | -783.2995 | -0.8106 | -0.7524 |
| 0.3442 | 0.98 | 300 | 0.2610 | -0.7248 | -2.6224 | 0.9170 | 1.8976 | -877.8102 | -783.4282 | -0.8110 | -0.7528 |
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}| Metric | Value |
|---|---|
| Avg. | 53.94 |
| AI2 Reasoning Challenge (25-Shot) | 50.77 |
| HellaSwag (10-Shot) | 74.24 |
| MMLU (5-Shot) | 60.70 |
| TruthfulQA (0-shot) | 42.37 |
| Winogrande (5-shot) | 68.11 |
| GSM8k (5-shot) | 27.45 |