# Install Hugging Face libraries %pip install --upgrade "transformers==4.40.0" "datasets==2.18.0" "accelerate==0.29.3" "evaluate==0.4.1" "bitsandbytes==0.43.1" "huggingface_hub==0.22.2" "trl==0.8.6" "peft==0.10.0"接下来，登录 Hugging Face 获取 Llama 3 70b 模型。创建...
# Install Hugging Face libraries %pip install --upgrade "transformers==4.40.0" "datasets==2.18.0" "accelerate==0.29.3" "evaluate==0.4.1" "bitsandbytes==0.43.1" "huggingface_hub==0.22.2" "trl==0.8.6" "peft==0.10.0" 接下来,登录 Hugging Face 获取 Llama 3 70b 模型。 创建和加载数据...
# Install Hugging Face libraries %pip install --upgrade "transformers==4.40.0" "datasets==2.18.0" "accelerate==0.29.3" "evaluate==0.4.1" "bitsandbytes==0.43.1" "huggingface_hub==0.22.2" "trl==0.8.6" "peft==0.10.0" 接下来,登录 Hugging Face 获取 Llama 3 70b 模型。 创建和加载数据...
# Install Hugging Face libraries %pip install --upgrade "transformers==4.40.0" "datasets==2.18.0" "accelerate==0.29.3" "evaluate==0.4.1" "bitsandbytes==0.43.1" "huggingface_hub==0.22.2" "trl==0.8.6" "peft==0.10.0" 1. 2. 3. 4. 接下来,登录 Hugging Face 获取 Llama 3 70b 模型。
# Install Hugging Face libraries %pip install --upgrade "transformers==4.40.0" "datasets==2.18.0" "accelerate==0.29.3" "evaluate==0.4.1" "bitsandbytes==0.43.1" "huggingface_hub==0.22.2" "trl==0.8.6" "peft==0.10.0" 接下来,登录 Hugging Face 获取 Llama 3 70b 模型。
# Install Hugging Face libraries%pip install--upgrade"transformers==4.40.0""datasets==2.18.0""accelerate==0.29.3""evaluate==0.4.1""bitsandbytes==0.43.1""huggingface_hub==0.22.2""trl==0.8.6""peft==0.10.0" 接下来,登录 Hugging Face 获取 Llama 3 70b 模型。
pip install"torch==2.1.2"tensorboard# 安装Hugging Face库!pip install --upgrade\"transformers==4.36.2"\"datasets==2.16.1"\"accelerate==0.26.1"\"evaluate==0.4.1"\"bitsandbytes==0.42.0"\# "trl==0.7.10" # \# "peft==0.7.1" \# 从github安装peft & trl!pip install git+https://...
阅读《评估 LLMs 和 RAG,一个使用 Langchain 和 Hugging Face 的实用案例》可以了解到关于评估生成模型的相关内容。 文章地址:philschmid.de/evaluate- import torch from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer peft_model_id = "./llama-3-70b-hf-no-robot" # Load ...
accuracy.append(evaluate(sample)) print(f"Accuracy: {sum(accuracy)/len(accuracy)}")服务 对于生产用例,我们推荐 Hugging Face 的文本生成推理。只需几行代码,我们就可以将模型容器化以实现可扩展的部署: dockerrun -p 8080 : 8080 \ -v my_model:/workspace \ ...
Hugging Face 提供了 evaluate 库来计算模型的评估指标。例如,我们可以使用准确率(accuracy)作为评估指标。以下代码展示了如何使用 evaluate 库计算模型的准确率: import numpy as np import evaluate # 加载准确率指标 metric = evaluate.load("accuracy")