菜单
OpenAI CLIP — contrastive language-image pre-training. Zero-shot image classification, image-text similarity, concept search, and cross-modal retrieval. Embed images and text into shared space.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mkurman/zorai --skill clip命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use when provisioning Prime Intellect GPU compute, managing pods/disks/sandboxes, running hosted RL training via prime lab, installing or publishing RL environments, or exposing local services via Prime Tunnel. Covers the `prime` CLI (PyPI: prime) for all Prime Intellect platform operations.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
基于 SOC 职业分类
正在显示 SKILL.md
| name | clip |
| description | OpenAI CLIP — contrastive language-image pre-training. Zero-shot image classification, image-text similarity, concept search, and cross-modal retrieval. Embed images and text into shared space. |
| tags | ["clip","multimodal","image-text","zero-shot","embeddings","openai","zorai"] |
OpenAI CLIP (Contrastive Language-Image Pre-training) learns joint text-image representations. Enables zero-shot image classification, image-text similarity, cross-modal search, and image captioning without task-specific training.
uv pip install openai-clip
import clip
import torch
model, preprocess = clip.load("ViT-B/32")
image = preprocess(load_image("photo.jpg")).unsqueeze(0)
text = clip.tokenize(["a dog", "a cat", "a bird"])
with torch.no_grad():
logits, _ = model(image, text)
probs = logits.softmax(dim=-1)
print(f"Predicted: class {probs.argmax().item()} with {probs.max():.2%} confidence")
images = torch.stack([preprocess(img) for img in [load_image("a.jpg"), load_image("b.jpg")]])
texts = clip.tokenize(["sunset", "ocean", "mountain"])
with torch.no_grad():
similarity = model(images, texts)[0].softmax(dim=-1)