问题描述: 使用peft微调llama3 8b,训练代码基本是按照样例稍作修改,在训练的时候 前10个steps,loss稍高,后面输出的loss,一直都是0.0了 微调代码: import torch from datasets import Dataset import pandas as pd from transformers import AutoTokenizer, AutoModel
# 导入文本拆分器、文档加载器、向量存储和OpenAI嵌入模块 from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import WebBaseLoader from langchain_community.vectorstores import Chroma from langchain_openai import OpenAIEmbeddings # 定义要加载的网页URL列表 ...
embeddings = hidden_states[-1] sentence_embedding = embeddings.mean(dim=1) return perplexity.to('cpu'), sentence_embedding.to('cpu') # Used to get the ppl and emb for part of input, used in conditional version, and token-wise loss def get_perplexity_and_embedding_part_text(tokenizer, m...
embedding_function=embeddings ) llm = Atom(model_path = "/root/autodl-tmp/FlagAlpha") template = """ {context} 问题: {question} 有用的回答:""" QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context","question"], template=template) # 运行 chain from langchain.chains import Retrieval...
This article codes the self-attention mechanisms used in transformer architectures and large language models (LLMs) such as GPT-4 and Llama from scratch in PyTorch.
technique involves using a smaller model that can achieve comparable performance with a fraction of the computational resources. The main idea behindknowledge distillationis to transfer advanced capabilities from leading proprietary LLMs, such as GPT-4, to their open-source counterparts like LLaMA and ...
from peft import LoraConfig, TaskType, get_peft_model def process_func(example): MAX_LENGTH = 384 # Llama分词器会将一个中文字切分为多个token,因此需要放开一些最大长度,保证数据的完整性 input_ids, attention_mask, labels = [], [], [] instruction = tokenizer(f"<|begin_of_text|><|start_...
embedding=GPT4AllEmbeddings(), ) retriever = vectorstore.as_retriever() ### Retrieval Grader from langchain.prompts import PromptTemplate from langchain_community.chat_models import ChatOllama from langchain_core.output_parsers import JsonOutputParser ...
self.get_model().vision_tower ipdb> [CLIPVisionTower( (vision_tower): CLIPVisionModel( (vision_model): CLIPVisionTransformer( (embeddings): CLIPVisionEmbeddings( (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False) (position_embedding): Embedding(257, ...
[string]) -> string // Convenience function for Base64-encoding the given file base64EncodeFile(string) -> string // Describe the given base64-encoded image using Ollama (and the `llava-llama3` model, by default) describeImage(string, [string]) -> string // Given two embeddings (...