Search Results for "context"

...A Workspace functions a lot like a thread, but with the addition of containerization of your documents. Workspaces can share documents, but they do not talk to each other so you can keep your context for each workspace clean.

...Instead of relying on complex frameworks or cloud services, the repository demonstrates the entire RAG pipeline using transparent and minimal implementations. The project walks through key concepts such as generating embeddings, building vector databases, retrieving relevant documents, and integrating the retrieved context into language model prompts. Each example is written with detailed explanations so that developers can understand the internal mechanics of semantic search and context-aware language generation. The repository emphasizes learning through direct implementation, allowing users to see how each component of the RAG architecture functions independently.

DSH Anchored Standard is a collection of experimental DeepSeek Harness presets designed to combine Minimal-style reasoning behavior with access to broader Standard tools. Its base mode begins a new session with only the real Minimal bash and text-editing tools while suppressing automatically injected context. After a durable tool call or assistant response, the preset promotes the session to a resident phase with discovery tools and restored Standard context. Additional modes experiment with zero-tool anchors, seeded trajectories, permanent Minimal catalogs, and separate thinking and execution stages. Promotion state is derived from persistent session events, so it survives reloads and resumed sessions. ...

...The project focuses on calculating GPU memory requirements and predicted inference speed for different models, hardware configurations, and quantization strategies. By analyzing factors such as model size, context length, batch size, and GPU specifications, the system estimates how much VRAM will be required and how fast tokens can be generated during inference. The tool also provides a detailed breakdown of where GPU memory is allocated, including model weights, KV cache, activations, and other runtime overhead. This information allows developers to evaluate trade-offs between different quantization methods such as GGML, bitsandbytes, and QLoRA before attempting to deploy a model. gpu_poor is particularly useful for researchers and hobbyists.