Introduce books/ with civitai-krea2 and HF fictext builders, sha-diff seed to VM, and drop train.jsonl from the civitai scrape pipeline.
Co-authored-by: Cursor <cursoragent@cursor.com>
Replace assistent-personas overlay seed with assistent-extensions SFTP and an assistent: section in extensions.yaml so personalities install like other extensions without private URLs in the public repo.
Co-authored-by: Cursor <cursoragent@cursor.com>
Align architecture/cli/decisions with modules and Debug API; cross-link seed overlays and scrape→FTS so user docs match 0.2.0.
Co-authored-by: Cursor <cursoragent@cursor.com>
Local civitai-dataset launchers collect ~2000 prompt/params rows without images; search.jsonl is pushed on up for cheap example lookup.
Co-authored-by: Cursor <cursoragent@cursor.com>
Write preferred chat tag into Assistent/ollama-roles.json so UI and warmup align with manifest default: true.
Co-authored-by: Cursor <cursoragent@cursor.com>
gpu-rent now writes personas/<id>/ on the VM (not legacy personas.json), adds seed-personas/doctor checks, and shortens mid/high keep-alive now that Assistent parks the LLM before Generate.
Co-authored-by: Cursor <cursoragent@cursor.com>
Pull nomic-embed-text for Assistent memory, write ollama-roles.json, CPU Modelfile, and raise MAX_LOADED_MODELS/NUM_PARALLEL to 2.
Co-authored-by: Cursor <cursoragent@cursor.com>
A 1-token /api/chat after tags (and again if /api/ps is empty) loads VL weights before the first message. Mid KEEP_ALIVE is 15m so a short image-gen burst does not unload the model.
Co-authored-by: Cursor <cursoragent@cursor.com>
- Enhanced the `OllamaTune` class to include a new `context_length` attribute, improving the configuration for different GPU tiers.
- Updated performance tuning logic to set appropriate context lengths for low, mid, high, and ultra tiers, ensuring optimal resource allocation.
- Modified installation scripts to reflect the new context length settings, enhancing the installation process for Ollama.
- Revised documentation to include context length details in the GPU performance table, providing clearer guidance for users.
- Added tests to validate the correct context length settings in various scenarios, ensuring robustness in performance tuning.
- Updated the `provision_llm` function to utilize the `/api/tags` endpoint for verifying available models, improving accuracy in model management.
- Introduced a new `already_have_ollama_tag` function to ensure exact tag matching, preventing mismatches during model checks.
- Enhanced the `pull_stream` function to require a successful status from the API before proceeding, ensuring reliable model downloads.
- Added logic to handle unwritten blob files, improving the robustness of the model pulling process.
- Updated documentation and tests to reflect these changes, ensuring clarity and reliability in Ollama model operations.
- Revised model descriptions in `ollama-models.example.yaml` to emphasize uncensored and abliterated requirements, enhancing user understanding.
- Updated documentation in `llm.md` to reflect changes in model tags and their meanings, ensuring accurate guidance for users.
- Modified CLI help messages to clarify the nature of presets, reinforcing that all options are abliterate models with Russian support.
- Enhanced the `llm_runtime.py` file to align preset labels with the updated model descriptions, improving consistency across the codebase.
- Removed references to llamacpp from configuration files, scripts, and documentation, streamlining the LLM setup process to focus solely on Ollama.
- Updated environment variables and paths to eliminate llamacpp-related entries, ensuring clarity in the configuration.
- Adjusted CLI commands and help messages to reflect the removal of llamacpp, enhancing user experience and reducing confusion.
- Revised documentation to provide clear guidance on using Ollama exclusively, including updates to setup instructions and runtime options.
- Added a new configuration option `UP_STOP_ON_FAIL` to control whether the GPU should be stopped automatically if the `up` command fails, enhancing user control over resource management.
- Updated the CLI to include a `--keep-on-fail` flag, allowing users to prevent GPU shutdown during installation errors.
- Enhanced the installation scripts and documentation to reflect these changes, providing clearer guidance on the new behavior and configuration options.
- Improved error handling in the CLI to ensure proper cleanup of resources in case of failure, preventing unexpected billing for unused GPU resources.
- Added new environment variables in `env.example` and `gpu-rent.vars.example` for fine-tuning LLM settings, including CUDA build options and model version pinning.
- Updated `llm.md` documentation to include detailed descriptions of new configuration options and usage cases for LLM setups.
- Enhanced the `provision.py` script to forward new environment variables during remote installations, improving the installation process for LLM components.
- Modified the `install_llamacpp.sh` script to support conditional CUDA builds and asset URL overrides, ensuring better compatibility with various environments.
- Improved logging in the installation scripts to provide clearer feedback during the setup process.
- Added a new function `pick_llamacpp_linux_asset_url` to select appropriate Linux release assets, prioritizing Ubuntu CUDA and Vulkan options while excluding Windows and macOS binaries.
- Updated the installation script to build `llama-server` from source when Linux CUDA binaries are unavailable, improving compatibility and flexibility.
- Revised documentation to reflect changes in asset handling and installation procedures.
- Added tests to validate the new asset selection logic, ensuring correct behavior in various scenarios.
- Revised model URLs and descriptions in `llamacpp-models.example.yaml` and `ollama-models.example.yaml` to reflect new recommendations and vision capabilities.
- Updated the LLM runtime logic to support vision projectors and improved model resolution handling.
- Enhanced the installation script to conditionally include vision projectors when available.
- Added tests to validate the inclusion of vision projectors in model presets and ensure proper URL remapping for deprecated models.
- Improved documentation to clarify model usage and configuration options.
- Introduced support for Hugging Face API integration, allowing fallback model resolution when Civitai fails.
- Updated configuration to include `HF_TOKEN` and `HF_TOKEN_PATH` for authentication.
- Enhanced model capture logic to differentiate between Civitai and Hugging Face sources.
- Improved error handling for model downloads, providing clearer messages for authentication issues.
- Updated documentation to reflect new environment variables and usage instructions for Hugging Face integration.
- Added tests to validate the new fallback mechanism and ensure robust model resolution.
- Updated `env.example` and `gpu-rent.vars.example` to include new variables for LLM runtime and SwarmUI options.
- Refactored CLI commands to support interactive selection of LLM runtime and workload type (SwarmUI, LLM, or both).
- Improved access link generation to handle cases where SwarmUI is disabled, providing clearer user feedback.
- Enhanced provisioning logic to conditionally bootstrap SwarmUI based on user configuration, allowing for LLM-only setups.
- Updated documentation across multiple files to reflect changes in LLM integration, CLI usage, and configuration management.
- Added support for `llamacpp-models.yaml` in `.gitignore` and implemented logic to copy it in `gpu-rent.ps1` and `gpu-rent.sh`.
- Enhanced CLI to prompt for llama.cpp model presets during setup and execution, improving user experience.
- Updated configuration handling to include `llamacpp_models_manifest` and related functions for managing llama.cpp models.
- Improved documentation in `cli.md` and `llm.md` to reflect changes in llama.cpp integration and model management.
- Refactored provisioning logic to handle llama.cpp model downloads and configurations effectively.
- Introduced GPU probing functionality to gather and store GPU specifications in `/mnt/swarm_data/.gpu-rent-gpu.json`, aiding in performance tuning.
- Updated `install_ollama.sh` and `install_llamacpp.sh` to utilize GPU information for configuring optimal runtime parameters.
- Enhanced `provision.py` to include GPU probing and performance tuning logic, ensuring better resource allocation for LLM operations.
- Improved documentation in `decisions.md`, `llm.md`, and `swarmui.md` to reflect changes in GPU handling and performance tuning processes.
- Added new tests to validate the GPU probing and model resolution logic, ensuring robustness in handling various GPU configurations.
- Added support for a new extension, `swarm-assistent`, in `extensions.example.yaml` with a requirement for `ollama`.
- Enhanced the README.md to clarify the setup process and provide a quick start guide for using extensions.
- Updated documentation in `llm.md` to reflect the opt-in nature of LLM support and provide clearer instructions for enabling it.
- Improved the `autocomplete.md` to detail the automatic setup of word lists during the initial launch.
- Revised `cli.md` to include new commands and options related to LLM runtime handling and extension management.
- Enhanced the `spike-notes.md` to guide users through the first live run with a focus on LLM integration.
- Updated `resolve_llm_runtime` to prioritize live configuration over legacy notes, ensuring accurate runtime resolution.
- Enhanced `tunnel_forwards` to prefer current configuration for LLM runtime, improving tunnel setup logic.
- Improved idle-killer logic to handle stale markers and provide clearer warnings in the status output.
- Updated CLI documentation in `cli.md` to reflect changes in command behavior and runtime handling.
- Enhanced tests to validate new runtime resolution logic and ensure proper handling of configuration states.
- Added `ollama-models.yaml` to .gitignore and implemented logic to copy it in gpu-rent.ps1 and gpu-rent.sh.
- Enhanced env.example to include new variables for LLM runtime options and local watchdog configuration.
- Updated CLI commands to support LLM options during setup and execution, including new flags for Ollama and llama.cpp.
- Improved documentation in cli.md and README.md to reflect changes in LLM integration and local watchdog functionality.
- Adjusted architecture and decisions documentation to clarify the role of LLMs and local watchdog in the system.
Leonid PershinandCursor