CUDA_VISIBLE_DEVICES=0 vllm serve 7embed --dtype auto --api-key \ sk-1dwqsdv4r3wef3rvefg34ef1dwRv --tensor-parallel-size 1 \ --max-model-len 32768 --enforce-eager \ --disable-custom-all-reduce --port 7777 --ser
psg_out= self.model(**features, return_dict=True)#先把input通过model的forward求embeddingp_reps = self.sentence_embedding(psg_out.last_hidden_state, features['attention_mask'])#再求整个句子的embeddingifself.normlized:#归一化,利于下一步求cosin或dot productp_reps = torch.nn.functional.normalize...
To address these issues, this paper proposes the Conan-Embedding Model, maximizes the utilization of more and higher-quality negative examples. Specifically, we iteratively mine hard negatives during training, allowing the model to dynamically adapt to changing training data. Additionally, we introduce ...
当文本通过嵌入模型传递时,会产生包含嵌入的向量。下面是来自开源嵌入模型 sentence-transformers/all-MiniLM-L6-v2 以及 OpenAI 模型 text-embedding-3-small 的示例。from sentence_transformers import SentenceTransformersentences = ["Apple is a fruit", "Car is a vehicle"]model = SentenceTransformer('sentence...
self.embedding_model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1') self.chroma_client = chromadb.Client() self.collection = self.chroma_client.create_collection(name=collection_name) # 该方法用于将输入的数据集转换为向量,并保存到向量数据库中 ...
db=lancedb.connect("db")embeddings=[]ids=[]fori,iteminenumerate(data):embeddings.append(embedding_model(item))ids.append(i)df=pd.DataFrame({"id":ids,"embedding":embeddings})tbl=db.create_table("tbl",data=df)...tbl=db.open_table("tbl")sim=tbl.search(embedding_model(data)).metric("...
下面是来自开源嵌入模型 sentence-transformers/all-MiniLM-L6-v2 以及 OpenAI 模型 text-embedding-3-small 的示例。 from sentence_transformers import SentenceTransformer sentences = ["Apple is a fruit", "Car is a vehicle"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') ...
[ Full vs Embedding-only ]:Embedding-only 方法只在 embedding 层添加前缀向量并优化,而 Full 代表的 Prefix-tuning 不仅优化 embedding 层添加前缀参数,还在模型所有层的激活添加前缀并优化。实验得到一个不同方法的表达能力增强链条:discrete prompting < embedding-only < prefix-tuning。同时,Prefix-tuning 可以直...
同时,我们实现了一个BigramLanguageModel类,这模仿pytorch里的nn.Module写法,即: 1.参数在__init__中初始化; 2.推理在forward函数中实现,并通过__call__允许对象被直接调用; 3.序列生成在generate函数中实现; 最后,我们修改了数据加载的机制,如下:
稳定性:weight decay、gradient clipping。LLM训练的时候还会碰到loss spike问题,有些简单的解决办法就是重新训练,重最近的一个checkpoint开始,跳过发生loss spike的数据。GLM工作中发现,embedding layer中不正常的梯度会导致这个问题,通过缩减这个梯度,会环节spike的问题 ...