技能目录

为 AI Agent 发现可复用技能。

按任务搜索真实的 GitHub 技能,并在使用前查看 Stars、信任、审计、分类和安装路径。

每个推荐都保留与其仓库、审计和安装路径的明确关联。

搜索结果: falcon-7b

英文目录

Fara-7B: An Efficient Agentic Model for Computer Use

6.0K
Stars
77/100
信任
分类: automation审计

Lightweight inference library for ONNX files, written in C++. It can run Stable Diffusion XL 1.0 on a RPI Zero 2 (or in 298MB of RAM) but also Mistral 7B on desktops and servers. ARM, x86, WASM, RISC-V supported. Accelerated by XNNPACK. Python, C# and JS(WASM) bindings available.

2.1K
Stars
76/100
信任
分类: ml-automation审计

Chinese-LLaMA 1&2、Chinese-Falcon 基础模型;ChatFlow中文对话模型;中文OpenLLaMA模型;NLP预训练/指令微调数据集

3.0K
Stars
68/100
信任
分类: support-automation审计

🤖 Deploy a private ChatGPT alternative hosted within your VPC. 🔮 Connect it to your organization's knowledge base and use it as a corporate oracle. Supports open-source LLMs like Llama 2, Falcon, and GPT4All.

1.6K
Stars
71/100
信任
分类: support-automation审计

Ovis-Image is a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational constraints.

321
Stars
69/100
信任
分类: media-automation审计

Local AI talk with a custom voice based on Zephyr 7B model. Uses RealtimeSTT with faster_whisper for transcription and RealtimeTTS with Coqui XTTS for synthesis.

724
Stars
63/100
信任
分类: support-automation审计

This repository implements the idea of "caption upsampling" from DALL-E 3 with Zephyr-7B and gathers results with SDXL.

159
Stars
61/100
信任
分类: media-automation审计

Harden GitHub Actions CI/CD workflows for supply-chain security — SHA-pin actions, least-privilege token permissions, verified toolchain installs, OpenSSF Scorecard, and SLSA provenance. Use when adding or auditing GitHub Actions workflows, before making a repository public, when a supply-chain review flags CI gaps, or when standardizing CI hardening across GitHub projects. GitHub-specific by design — GitLab CI and Forgejo Actions are out of scope.

14
Stars
62/100
信任
分类: security审计

Design and build AI agents with persistent memory, tool use, and multi-turn conversation. Covers architecture selection, memory design, model selection, tool configuration, and implementation patterns across agent frameworks. Use when creating, debugging, or improving AI agents.

14
Stars
58/100
信任
分类: design-creative审计