Search Results for "token" - Page 7
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Showing 216 open source projects for "token"
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LaVague
Framework for building AI agents that automate complex web tasks
LaVague is an open source framework designed to help developers build AI-powered web agents capable of automating tasks across websites and web applications. It implements the concept of a Large Action Model framework, allowing agents to interpret a user-provided objective and translate it into a sequence of actions performed in a browser. These agents can navigate web pages, retrieve information, fill out forms, and execute multi-step workflows automatically. LaVague is centered around a... -
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minbpe
Minimal, clean code for the Byte Pair Encoding (BPE) algorithm
...The repository is structured as a teaching-oriented implementation that shows how to train a tokenizer by learning merge rules, then apply those merges to encode text into token IDs and decode tokens back into text. It is intentionally small and readable so developers can understand each stage of BPE, including the mechanics of pair counting, merge application, and vocabulary growth. The project is especially useful for practitioners who want to demystify how LLM tokenizers work or who need a lightweight reference implementation for experimentation. -
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Grok-1
Open-source, high-performance Mixture-of-Experts large language model
Grok-1 is a 314-billion-parameter Mixture-of-Experts (MoE) large language model developed by xAI. Designed to optimize computational efficiency, it activates only 25% of its weights for each input token. In March 2024, xAI released Grok-1's model weights and architecture under the Apache 2.0 license, making them openly accessible to developers. The accompanying GitHub repository provides JAX example code for loading and running the model. Due to its substantial size, utilizing Grok-1 requires a machine with significant GPU memory. ... -
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Mentat
Mentat - The AI Coding Assistant
...Mentat uses Git, so if your project doesn't already have Git set up, run git init. List the files you would like Mentat to read and edit as arguments. Mentat will add each of them to context, so be careful not to exceed the GPT-4 token context limit. -
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ClawBridge
The OpenClaw Mobile Dashboard.
The OpenClaw Mobile Dashboard. Monitor agent's real-time thoughts, actions, track token costs, and manage tasks from anywhere using your pocket-sized Mission Control. -
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Qwen of Death is a desktop coding assistant with GUI powered by the Qwen API. Users supply their own API key — all billing is handled directly with Openrouter.ai.
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GPT of Death is a desktop coding assistant with GUI powered by the OpenAI API. Users supply their own API key — all billing is handled directly with OpenAI.
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Grok of Death is a desktop coding assistant with GUI powered by the Grok API. Users supply their own API key — all billing is handled directly with xAI.
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Gemini of Death is a desktop coding assistant with GUI powered by the Googles Gemini API. Users supply their own API key — all billing is handled directly with Google.
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QOTD Discord Bot
Simple Question Of The Day (QOTD) Discord Bot for your server
...It allows users to add their own QOTD to the bot's queue, and QOTD managers to manage the queue. How to set it up? Simply follow a tutorial to create a Discord bot and run the code. Pass in the bot's token and configure the bot through the commands in qotd help. Restrictions? - The bot will only work on one server - The bot must be self-hosted -
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DeiT (Data-efficient Image Transformers)
Official DeiT repository
DeiT (Data-efficient Image Transformers) shows that Vision Transformers can be trained competitively on ImageNet-1k without external data by using strong training recipes and knowledge distillation. Its key idea is a specialized distillation strategy—including a learnable “distillation token”—that lets a transformer learn effectively from a CNN or transformer teacher on modest-scale datasets. The project provides compact ViT variants (Tiny/Small/Base) that achieve excellent accuracy–throughput trade-offs, making transformers practical beyond massive pretraining regimes. Training involves carefully tuned augmentations, regularization, and optimization schedules to stabilize learning and improve sample efficiency. ... -
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Claude of Death is a desktop coding assistant with GUI powered by the Anthropic Claude API. Users supply their own API key — all billing is handled directly with Anthropic. 4-24-26: Just added memory to it, like Claude Code. Also improved the save function. 4-25-26: Fixed save function for Macs. 4-28-26: Added Save Code as File, in addition to already present Generate File. 5-9-26: Renamed from Code of Death to Claude of Death.
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Zylthra
Zylthra: A PyQt6 app to generate synthetic datasets with DataLLM.
Welcome to Zylthra, a powerful Python-based desktop application built with PyQt6, designed to generate synthetic datasets using the DataLLM API from data.mostly.ai. This tool allows users to create custom datasets by defining columns, configuring generation parameters, and saving setups for reuse, all within a sleek, dark-themed interface. -
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GLM-4-32B-0414
Open Multilingual Multimodal Chat LMs
GLM-4-32B-0414 is a powerful open-source large language model featuring 32 billion parameters, designed to deliver performance comparable to leading models like OpenAI’s GPT series. It supports multilingual and multimodal chat capabilities with an extensive 32K token context length, making it ideal for dialogue, reasoning, and complex task completion. The model is pre-trained on 15 trillion tokens of high-quality data, including substantial synthetic reasoning datasets, and further enhanced with reinforcement learning and human preference alignment for improved instruction-following and function calling. ... -
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HasMCP
Convert API into MCP Server in seconds
HasMCP empowers AI development by seamlessly connecting your existing APIs to Large Language Models. Its Automated OpenAPI Mapping instantly translates API documentation into LLM-usable tools, eliminating manual coding. Security is paramount, with Native MCP Elicitation Auth managing complex authentication flows like OAuth2, ensuring user credentials are never exposed. To enhance efficiency, Context Window Optimization intelligently prunes API responses using JMESPath and Goja (JS) logic,... -
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SuperAGI
A dev-first open source autonomous AI agent framework
...Connect to multiple Vector DBs to enhance your agent’s performance. Each agent is unique, use different models of your choice. Get insights into your agent’s performance and optimize accordingly. Control token usage to manage costs effectively. Enable your agents to learn and adapt by storing their memory. Get notified when agents get stuck in the loop, and provide proactive resolution. Read and store files generated by Agents. -
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autollm
Ship RAG based LLM web apps in seconds
autollm is an open-source Python framework designed to make it much faster to build retrieval-augmented generation applications and expose them as usable services with minimal setup. The project focuses on simplifying the usual stack of model selection, document ingestion, vector storage, querying, and API deployment into a more unified developer experience. Its core idea is that a developer can create a query engine from a document set in just a few lines and then turn that same engine into... -
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Extended Dreambooth How-To Guides
Implementation of Dreambooth
...It focuses heavily on usability, offering detailed guides for different setups while still exposing advanced configuration options for experienced users. The system allows users to bind a unique token to a subject, which can later be used in prompts to generate consistent and recognizable outputs across different contexts. It also supports captioning, multi-subject training, and regularization techniques to improve generalization and avoid overfitting. -
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Mixtral offloading
Run Mixtral-8x7B models in Colab or consumer desktops
...The project implements techniques that allow model components to be dynamically moved between CPU memory and GPU memory during inference, significantly reducing the amount of GPU VRAM required to run the model. This approach takes advantage of the sparse activation properties of mixture-of-experts architectures, where only a subset of expert networks are used for each token during generation. By selectively loading and caching the required experts, the system avoids keeping the entire model in GPU memory at once. The repository includes notebooks and code examples that demonstrate how to run large language models on consumer hardware such as personal GPUs or cloud notebook environments. -
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ChatGPT-Reviewer
Automated pull requests reviewing and issues triaging with ChatGPT
...The ChatGPT reviewer PRs are also getting reviewed by ChatGPT, refer the pull requests for the sample review comments. In order to protect public repositories for malicious users, Github runs all pull request workflows raised from repository forks with a read-only token and no access to secrets. -
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gpu_poor
Calculate token/s & GPU memory requirement for any LLM
gpu_poor is an open-source tool designed to help developers determine whether their hardware is capable of running a specific large language model and to estimate the performance they can expect from it. 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... -
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Prompt-to-Prompt
Latent Diffusion and Stable Diffusion Implementation
...The method supports gentle edits (e.g., style, color, lighting) as well as stronger semantic substitutions, and it can localize edits to specific words or regions by selectively updating attention. Because edits are steerable via prompt wording and token weighting, creators can iterate quickly, exploring variations without losing composition. The repository includes reference notebooks and scripts that plug into popular latent diffusion backbones, making it practical to try the technique on your own prompts and seeds. It’s especially useful for workflows that need consistent framing, product shots, illustrations, and concept art, etc. -
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Petals
Run 100B+ language models at home, BitTorrent-style
...Run large language models like BLOOM-176B collaboratively — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning. Single-batch inference runs at ≈ 1 sec per step (token) — up to 10x faster than offloading, enough for chatbots and other interactive apps. Parallel inference reaches hundreds of tokens/sec. Beyond classic language model APIs — you can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of PyTorch. ... -
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FastViT
This repository contains the official implementation of research
...Its design pursues a favorable latency-accuracy Pareto curve, targeting edge devices and server scenarios where throughput and tail latency matter. The models use lightweight attention and carefully engineered blocks to minimize token mixing costs while preserving representation power. Training and inference recipes highlight straightforward integration into common vision tasks such as classification, detection, and segmentation. The codebase provides reference implementations and checkpoints that make it easy to evaluate or fine-tune on downstream datasets. In practice, FastViT offers drop-in backbones that reduce compute and memory pressure without exotic training tricks. -
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ToMe (Token Merging)
A method to increase the speed and lower the memory footprint
ToMe (Token Merging) is a PyTorch-based optimization framework designed to significantly accelerate Vision Transformer (ViT) architectures without retraining. Developed by researchers at Facebook (Meta AI), ToMe introduces an efficient technique that merges similar tokens within transformer layers, reducing redundant computation while preserving model accuracy.
