“transformers”的搜索结果

将PyTorch训练代码整理为LightningModule和Trainer结构，配置验证、日志记录、模型保存及多GPU分布式训练。

• 迁移现有PyTorch训练代码
• 添加验证指标记录和模型保存
• 配置多GPU训练

使用Qdrant将文本等内容表示为向量，实现相似内容搜索。支持按类别或日期筛选、为AI回答检索资料、实时推荐，以及内存和分布式部署的配置调整。

• 为AI回答检索参考资料
• 按类别和日期筛选相似内容
• 实现实时推荐

指导通过评价回答的强化学习对大语言模型进行后训练。结合Megatron-LM与SGLang，涵盖数据准备、并行执行及自定义生成流程。

• 开始语言模型的GRPO训练
• 并行运行回答生成与训练
• 训练使用工具的智能体

用Python实现文字生成图像、现有图像风格转换和指定区域补全，并指导如何控制构图与风格、调整生成参数及减少内存占用。

• 根据文字生成图像
• 将现有图像转为水彩风格
• 补全照片中的指定区域

以少量修改让现有PyTorch训练代码适用于多GPU或多台机器，指导混合精度、节省内存的DeepSpeed与FSDP配置，以及训练启动方法。

• 将单GPU训练迁移至多GPU
• 配置混合精度训练
• 通过DeepSpeed或FSDP节省内存

使用Chroma构建按语义相似度检索文档的系统，保存和更新正文及附加信息，按条件筛选结果，并用于开发结合检索资料的AI应用原型。

• 按语义相似度搜索文档
• 试作基于检索资料的AI回答
• 将文档数据保存在本地

通过正则表达式和语法规则约束本地语言模型的输出，生成符合指定格式的JSON、日期和固定分类标签，并用Python构建可重复使用的多阶段生成流程。

• 生成符合指定结构的JSON
• 统一日期和电子邮箱地址格式
• 使用固定标签对文本分类

将文本拆分为供AI模型使用的小单位，并转换为数字序列。支持批量处理、自定义词表训练、原文位置对应，以及通过截断和填充统一输入长度。

• 批量处理模型所需的文本输入
• 从文本文件训练自定义词表
• 将预测结果对应到原文位置

指导使用 AudioCraft 选择模型并实现从文字生成音乐、音效和环境声，涵盖旋律及参考音频引导、立体声输出和批量生成。

• 根据文字生成音乐
• 制作音效和环境声
• 使用旋律或参考音频生成音乐

Find scientific datasets, AI models, methodology posts, and interactive demos through the Hugging Science catalog. Compare candidates for a research task and verify their licenses, input formats, access requirements, and supported ways to run them.

中文简介正在准备中。当前显示英语简介。

• Finding models for protein or genome analysis
• Selecting datasets for scientific machine learning
• Comparing resource licenses and runtime requirements

Quantize diffusion transformers with NVIDIA ModelOpt, using FP8 or NVFP4 lower-precision numbers, and prepare the saved weights for SGLang Diffusion. Follow a workflow for loading, checking output quality, and comparing speed under matching conditions.

中文简介正在准备中。当前显示英语简介。

• Preparing ModelOpt FP8 weights for SGLang Diffusion
• Checking quality after diffusion model quantization
• Comparing BF16 and quantized generation speed

Screen AI application inputs and historical logs for prompt injection, where malicious instructions try to redirect the model. Combine pattern matching, text structure scoring, and a DeBERTa classifier to flag suspicious content and produce JSON results.

中文简介正在准备中。当前显示英语简介。

• Screening chatbot and AI agent inputs
• Checking external text used in retrieval-based AI applications
• Auditing historical interaction logs for injection attempts

Add checks to LLM prompts and responses using Llama Guard 3, NeMo Guardrails, and LLM Guard. Configure content screening, conversation boundaries, and sensitive-data filtering, then measure blocking and false-positive rates with labeled test prompts.

中文简介正在准备中。当前显示英语简介。

• Adding runtime safety checks to LLM, RAG, or agent apps
• Screening prompts for injection and jailbreak attempts
• Filtering sensitive or toxic model responses

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

中文简介正在准备中。当前显示英语简介。

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

中文简介正在准备中。当前显示英语简介。

• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Guides language model training with GRPO in TRL, comparing several responses and rewarding better results. Covers dataset preparation, scoring rules for correctness and format, training configuration, monitoring, and saving models for later use.

中文简介正在准备中。当前显示英语简介。

• Training models to follow JSON or XML formats
• Designing rewards for math and coding tasks
• Adjusting training to available GPU memory

Plan and configure large language model training with OpenRLHF to better match human preferences. Covers training method selection, distributed GPU setup, inference acceleration with vLLM, and troubleshooting memory or training stability issues.

中文简介正在准备中。当前显示英语简介。

• Choosing a method for training models on human preferences
• Setting up PPO training with a reward model
• Configuring GRPO training without a critic model

Build safety checks for LLM prompts and responses using Meta's LlamaGuard moderation model. Classify potentially harmful content, block flagged messages, serve checks through an API, and integrate them with NeMo Guardrails.

中文简介正在准备中。当前显示英语简介。

• Checking prompts before sending them to an LLM
• Filtering responses before displaying them
• Building a moderation API

Set up reinforcement learning, which trains models using rewards, with verl. Follow workflows for preparing data, configuring GRPO or PPO training, scaling across GPUs, and addressing memory errors or unstable training.

中文简介正在准备中。当前显示英语简介。

• Training a math reasoning model with GRPO
• Configuring PPO training with a critic model
• Scaling large model training across multiple nodes

Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.

中文简介正在准备中。当前显示英语简介。

• Preparing reward-based training for GLM or Qwen models
• Overlapping response generation and training
• Training agents that use tools across multiple turns

Train language models with SimPO using pairs of preferred and rejected answers. Configure preference training without a separate reference model, adapt settings for base or instruction models, and address unstable training, memory limits, or lost capabilities.

中文简介正在准备中。当前显示英语简介。

• Training a base model with preferred and rejected answers
• Further tuning an instruction model with preference data
• Adjusting training settings for math and code tasks

Build image-based conversations with LLaVA, an AI model that combines image understanding with language. Covers setup, model selection, image descriptions, follow-up questions, and memory-saving options for running models on a GPU.

中文简介正在准备中。当前显示英语简介。

• Building a chatbot that answers questions about images
• Generating image descriptions and captions
• Exploring a scene through follow-up questions

Build image generation workflows in Python with Stable Diffusion and HuggingFace Diffusers. The guide covers text prompts, transforming existing images, replacing selected regions, controlling composition, applying learned styles, and adjusting memory use during generation.

中文简介正在准备中。当前显示英语简介。

• Generating images from text descriptions
• Changing the style of an existing image
• Replacing a selected region of an image

Investigate why a PyTorch model behaves a certain way by changing its internal values with pyvene. The skill guides experiments that swap or remove activations, train interventions, compare outputs, and save or share intervention configurations.

中文简介正在准备中。当前显示英语简介。

• Locating model components that affect an output
• Swapping activations to test behavioral hypotheses
• Training interventions to investigate causal structure