V2L Tokenizer采用编码器-量化器-解码器结构。一共使用两个量化器: 一个局部量化器和一个全局量化器。每个量化器都与一个独立的、来自LLM词汇表的冻结codebook相关联。然后, 图像被量化为 K_g 个全局token和 K_l 个局部token, 分别从全局和局部codebook中提取。 全局codebook。LLM词汇表包括由语言Tokenizer生成的...
Run "step4_training_v2l_tokenizer.py" to train the V2L Tokenizer based on the codebook produced by the above 3 steps. We also provided our codebooks and checkpoints at:https://drive.google.com/drive/folders/1Z8GxE-WMEijJV-JZmqL7AGzsB0gHk4ow?usp=sharing ...
from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True,...
fromtransformersimportAutoTokenizer, AutoModelForSequenceClassificationimporttorch model = AutoModelForSequenceClassification.from_pretrained('model_name') tokenizer = AutoTokenizer.from_pretrained('model_name') features = tokenizer(['How many people live in Berlin?','How many people live in Berlin?'],...
从transformers导入AutoModelForCausalLM,AutoTokenizer 导入json 从日期时间导入日期时间 device=“cuda”#将模型加载到的设备模型=AutoModelForCausalLM.from_pretrained(“fireworks-ai/filfunction-v2”,device_map=“auto”)词元分析器=自动令牌化器.from_pretrained(“fireworks-ai/filfunction-v2”)函数_规范=[ {...
[tokenizers] Ensure that add_prefix_space is propagated to backend_tokenizer.pre_tokenizer (#35593) Setup loss_type in config at model init time (#34616) [docs] Update Python version in translations by@jla524in#35096 [docs] top_p, top_k, temperature docstrings by@stevhliuin#35065 ...
On inspection, I found that the same second dimension can be recovered from key 'cond_stage_model.model.ln_final.bias', and use that instead. I hope this is correct; tested on multiple v1, v2 and inpainting models and they converted correctly. ...
from modelscope import snapshot_download, AutoModel, AutoTokenizer import osmodel_dir = snapshot_download('deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct', cache_dir='/root/autodl-tmp', revision='master') ```终端出现下图结果表示下载成功。#...
tokenizer=tokenizer, max_length=max_length, dataset_map_fn=alpaca_map_fn, template_map_fn=dict( type=template_map_fn_factory, template=prompt_template), remove_unused_columns=True, shuffle_before_pack=True, pack_to_max_length=pack_to_max_length, use_varlen_attn=use_varlen_attn) sampler ...
『词法分析』新增jieba的paddle模式切词模型,可一键完成中文分词、关键词抽取等功能。 『语义表示』新增基于网页、小说、新闻三类大规模文本数据的LDA主题模型及其语义相似度计算接口。 Fine-tune API升级,提升灵活性并支持更多任务 新增Tokenizer API,支持更加灵活的切词、切字模式和自定义切词工具拓展。 新增文本生成任...