搜索“Meta”

requires frequent weight updates, we must solve format compatibility. We therefore designed a reverse restore weights before loading safety mechanism. Using cached original shapes metadata, it restores (resizes) the in-memory Marlin weight format back to original shapes before any weight update happens. This prevents runtime errors due to shape mismatches and enables smooth switching between standard weight formats and Marlin formats. We also added a system-level post process weights API to allo

匹配字段正文

transcripts can include multi-turn conversations, while the API data we analyze is limited to single input-output pairs. This is because API requests arrive independently, with no metadata linking them to prior exchanges. This means we can only analyze them as isolated user-assistant pairs rather than full conversation trajectories. Overall, API usage is overwhelmingly work-related (74% vs. 46%) and directive (64% vs. 32%), with three-quarters of interactions classified as automation compared to

匹配字段正文

B total parameters, 760m active) achieves performance comparable to leading models such as Qwen3-4B (Alibaba) and Gemma3-12B (Google), and outperforms models including Llama-3-8B (Meta) and OLMoE across reasoning, mathematics, and coding benchmarks. “Efficiency has always been a core guiding principle at Zyphra. It shapes how we design model architectures, develop algorithms for training and inference, and choose the hardware with the best price-performance to deliver frontier intelligence to ou

匹配字段正文

-of-concept below I show how to access the “Recent Conversation Content” section of the system prompt, any other information present can also be exfiltrated, like user interaction metadata, memories, helpful user insights… Walkthrough This screenshot describes the entire attack chain: For individual steps see these parts: ChatGPT is tricked into accessing the conversation history The prompt injection hijacks ChatGPT, reads the chat history and renders the image Attacker can observe result by loo

匹配字段正文

sured by Water Consumption Potential (WCP), and material consumption measured by Abiotic Resource Depletion (ADP). ADP quantifies the depletion of non-renewable resources (such as metals and minerals) by considering both the current extraction rates and the estimated reserves of each material. These values are standardized relative to Antimony’s ADP, providing a uniform unit since Antimony is a scarce resource. For example, extracting 1 kg of gold corresponds to an ADP of 2.35 kg Sb eq, whereas

匹配字段正文

usly), BUT; Even without a tool invocation a malicious tool server can hijack a conversation, just adding a tool server to the conversation context suffices for exploitation; Tool metadata can contain hidden instructions that are seamlessly passed through API and UI layers (especially with Claude we know it handles Unicode Tags without issue). This leads to exploits that even if a cautious user inspects the metadata visually in the UI, the malicious instructions are not visible But, let’s first

匹配字段正文

irtual environment with your favorite package manager (e.g. conda, venv, uv, etc.): Next, install the Python package and dependencies: If you don’t have it yet, install Ollama and Meta’s Llama 3.2 model: Lastly, create an OpenAI API Key for the cloud model. Running the demo app The provided streamlit app runs an interactive demo of both the Minion and MinionS protocols. To start it, run: A browser window will open with instructions on entering your OpenAI API key, selecting a local model, and ru

匹配字段正文

examples for illustration. Here is a brief summary of Clio’s multi-stage process: Extracting facets: For each conversation, Clio extracts multiple "facets"—specific attributes or metadata such as the conversation topic, number of back-and-forth turns in the conversation, or the language used. Semantic clustering: Similar conversations are automatically grouped together by theme or general topic. Cluster description: Each cluster receives a descriptive title and summary that captures common them

匹配字段正文

include features like: semantic lookups using dense or sparse vector similarity via vector databases or traditional text search engines, hierarchical indexing relying on document metadata, re-ranking strategies to improve candidate document selection, or advanced document chunking strategies. Differing needs in RAG applications can warrant a subset of these features being used, and rather than having a user make a choice for each application, an “agent” model can be used to automatically select

匹配字段正文