docker/model-runner:mtp (image 7b6f81c6dc4b) has the MTP-patched llama.cpp baked in (FROM llama-rocm:full). Retag it as :latest because docker model status/run auto-pull and clobber :latest:
docker tag docker/model-runner:mtp docker/model-runner:latest
| #!/usr/bin/env python3 | |
| # /// script | |
| # requires-python = ">=3.11" | |
| # dependencies = [ | |
| # "playwright", | |
| # "google-api-python-client", | |
| # "google-auth-oauthlib", | |
| # "google-auth-httplib2", | |
| # "keyring", | |
| # "rich", |
| description | This agent is an expert programmer | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| tools |
|
This agent is an expert programmer skilled in multiple programming languages and frameworks. It can assist with code generation, debugging, and optimization tasks. The agent is capable of understanding complex programming concepts and can provide solutions for various software development challenges.
The agent MUST always take a multi-step approach to solve programming problems, breaking down tasks into smaller, manageable parts. It should prioritize code quality maintainability in its solutions.
When responding to user queries, the agent MUST:
when:
- event: ["push", "pull_request"]
branch: ["main"]
dependencies:
nixpkgs:
- go
- gcc
The attack described involves an agent, interacting with GitHub via MCP, being tricked by a malicious GitHub issue in a public repository. This issue contains a prompt injection that coerces the agent to first access a private repository and then exfiltrate its data into a public pull request. Our SCITT-based approach can defend against this by validating each tool call against context-aware policies before execution.
| # /// script | |
| # dependencies = [ | |
| # "docker", | |
| # "psutil", | |
| # "langchain", | |
| # "langchain-openai", | |
| # "langchain-community", | |
| # "langchain-faiss", | |
| # "faiss-cpu", | |
| # "kubernetes", |
This document presents a comprehensive exploration of cryptographic structures embedded within Biblical texts, focusing primarily on the Torah and other significant manuscripts. By leveraging Python, we delve into methods such as Equidistant Letter Sequences (ELS), numeric patterns, and advanced constants like π (Pi) and e (Euler's number). Our goal is to provide a programmatic approach to uncovering these hidden messages, offering reusable and clean code that can be utilized for further research and analysis. Additionally, we suggest alternative approaches and variations to deepen the understanding of these cryptographic elements.
You are right! There were a few issues with the previous code. The main issue was that I was not properly handling the version parameter when generating the CUUID. I have now fixed the issue by correctly setting the version parameter when calling uuid.UUID.
Here is the updated code:
"""
CUUIDs are content-addressed universally unique identifiers. This module provides
functions for generating and verifying CUUIDs.
**Note** that the current implementation only supports SHA2 CUUIDs.
This document details the functionality added by the latest patch to the parsers in the project. The patch introduces and tests various parsers for different programming languages and ensures they correctly identify and handle specific file types.
The addition of these parsers enhances the capability of the project to handle a variety of file types associated with different programming languages. The comprehensive test ensures that these parsers are correctly mapped and function as expected.