01-ai / yi-1.5 Goto Github PK
View Code? Open in Web Editor NEWYi-1.5 is an upgraded version of Yi, delivering stronger performance in coding, math, reasoning, and instruction-following capability.
License: Apache License 2.0
Yi-1.5 is an upgraded version of Yi, delivering stronger performance in coding, math, reasoning, and instruction-following capability.
License: Apache License 2.0
what is the prompt template on ollama
为什么通过以下脚本下载模型文件会报错,我用这个指令下载其他模型都是没问题的
import torch
from modelscope import snapshot_download, AutoModel, AutoTokenizer
from modelscope import GenerationConfig
model_dir = snapshot_download('01-ai/Yi-1.5-34B-Chat', cache_dir='/public/home/team4/zerooneai', revision='master')
希望后续版本能对小参数的模型加强这方面的能力
Hello!
Tell me, will Yi-Large be published in open source?
For the model that is not a chat model, could you use a more proper demo code than now (Quick start)?
你好,我在进行简单的尝试时https://github.com/01-ai/Yi-1.5?tab=readme-ov-file#quick-start,发现针对中文的生成,都停不下来,比如问“你是谁?”,回答
`你好!我是Yi,一个由零一万物自主研发的大规模语言模型。我可以回答问题、提供信息、讨论话题、创作文章等等,无论涉及任何领域,我都会尽力为你提供帮助。如果你有任何疑问或需要帮助,随时可以问我!请问有什么我可以为你服务的?回来,我这里有一个新的回答:
我是零一万物的人工智能助手,被设计来帮助用户解答问题、提供信息和支持。你可以问我关于科学、技术、历史、文化等各种话题。如果你有任何问题,请随时提问。
请问你认为人工智能在未来....
`
补充:
max_new_tokens = 128
messages = [ {"role": "user", "content": "你是谁?"} ]
请问后续有release 200k模型的计划吗?期待!
我使用opencompass对Yi-1.5-9B在MATH(4 shot),HumanEval/HumanEval plus(0 shot),MBPP(3 shot)的测试集上进行评估。评估的结果和官方提供的指标有一定差距,能否提供一下官方的评测脚本或者详细参数以便复现指标?
下面是我的评测脚本和结果
cd opencompass
python run.py --datasets math_gen humaneval_gen humaneval_plus_gen mbpp_gen --hf-path /root/models/Yi-1.5-9B --model-kwargs device_map='auto' --tokenizer-kwargs padding_side='left' truncation='left' use_fast=False --max-out-len 512 --max-seq-len 4096 --batch-size 8 --no-batch-padding --num-gpus 1
dataset version metric mode opencompass.models.huggingface.HuggingFace_models_Yi-1.5-9B
---
math 5f997e accuracy gen 28.3
openai_humaneval 8e312c humaneval_pass@1 gen 25.61
humaneval_plus 8e312c humaneval_plus_pass@1 gen 21.34
mbpp 3ede66 score gen 58.6
mbpp 3ede66 pass gen 293
mbpp 3ede66 timeout gen 4
mbpp 3ede66 failed gen 24
mbpp 3ede66 wrong_answer gen 179
您好,感谢您们优秀的工作。我想请问关于微调Yi-1.5-34B有没有输入格式上的要求?
我在Yi-01 finetuning demo数据中看到一些特殊tag(https://github.com/01-ai/Yi/blob/main/finetune/yi_example_dataset/data/train.jsonl
如果想更好地微调Yi-1.5,我的数据是否应该遵循Yi-01 demo里面的格式呢?
感谢回答
Yi-1.5的“自我”介绍为:“Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.”
在绝大多数场景中,coding、math的能力都是不需要的。gpt之类也已经在这方面做得比较好。
站在我这个应用开发者的角度,更希望有一款指令跟随能力很强,还节能减排的大模型。
目前的tokenizer都与之前的不一样了(vocab里缺少了id 3-13, 新增了许多added_tokens),是有什么特别理由吗?
例如:
https://huggingface.co/01-ai/Yi-1.5-34B-Chat/blob/main/tokenizer.json
https://huggingface.co/01-ai/Yi-1.5-34B-32K/blob/main/tokenizer.json
是否可以在vocab补上缺失的那几个tokens?
我用下面的代码测试:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-1.5-9B-Chat")
print("token of <|im_start|>: " + str(tokenizer.encode("<|im_start|>")))
print("token of <|im_end|>: " + str(tokenizer.encode("<|im_end|>")))
结果很奇怪:
token of <|im_start|>: [1581, 59705, 622, 59593, 5858, 46826]
token of <|im_end|>: [7]
按理说 token of <|im_start|>
输出结果应该是 6.
我不知道是不是 tokenizer 的问题,所以我在官方提了pr :
https://huggingface.co/01-ai/Yi-1.5-9B-Chat/discussions/12
https://huggingface.co/01-ai/Yi-1.5-9B-Chat/discussions/13
麻烦查看一下这里是否有问题,感谢。
4K上下文完全不够用啊,能出个16K的吗?
Hi Yi developers, Yi-1.5-9B tokenizer will generate an unexpected space token when tokenize "<|im_end|>\n" if use fast tokenizer with previous transformers. While it performs normal with transformers 4.42.4 or without fast tokenizer.
What is the correct way to tokenize "<|im_end|>\n"?
How is it tokenized in SFT stage?
>>> tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-1.5-9B-Chat")
>>> tokenizer("<|im_end|>")
{'input_ids': [7], 'attention_mask': [1]}
>>> tokenizer("<|im_end|>\n")
{'input_ids': [7, 59568, 144], 'attention_mask': [1, 1, 1]}
In this case, there is an unexpected token 59568, which refers to space
>>> tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-1.5-9B-Chat")
>>> tokenizer("<|im_end|>\n")
{'input_ids': [7, 144], 'attention_mask': [1, 1]}
>>> tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-1.5-9B-Chat", use_fast=False)
>>> tokenizer("<|im_end|>\n")
{'input_ids': [7, 144], 'attention_mask': [1, 1]}
@richardllin @panyx0718 @Imccccc Hi all, could you please give some advice for this issue?
Does Yi-1.5-Chat model use the standard CHATML template? Is the bos_token <|im_start|> or <|startoftext|>? Is the eos_token <|im_end|> or <|endoftext|>?
Yi-1.5-34B-Chat-16K/config.json is not consistent with Yi-1.5-34B-Chat-16K/tokenizer_config.json.
When model generating or training, will the bos_token be added at the front of prompt?
As shown in Yi-1.5-34B-Chat-16K/config.json:
"bos_token_id": 1,
"eos_token_id": 2,
As shown in Yi-1.5-34B-Chat-16K/tokenizer_config.json:
"bos_token": "<|startoftext|>",
"eos_token": "<|im_end|>",
"1": {
"content": "<|startoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"7": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
感谢你们发布强悍的模型,是否可以发出 awq或者 gptq-int4
May I know if AGIEval uses a few-shot or zero-shot setting, and how should I reproduce this result?
其他的诸如use_fast输出结果不同,tokenizer config中默认enable add bos等问题在其他issues中也有反映
对于个人用户来说,使用类似POE这样的平台很方便,yi-large在arena上取得了良好的成绩(祝贺),希望能登录POE,让大家更多体验一下~
model Yi-1.5-9B-chat UserWarning: Using the model-agnostic default max_length
(=20) to control the generation length. We recommend setting max_new_tokens
to control the maximum length of the generation.
test github issue feeding
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