This skill assumes finetuning-method-selection already routed here because the data shape is preference pairs or unpaired thumbs-up/down feedback, not demonstrations (that's lora-qlora-recipes) or a verifiable reward signal (that's grpo-rlvr-training). What follows is metho. Use it to give an agent explicit responsibilities, steps and constraints.
This skill assumes finetuning-method-selection already routed here because the target behavior has a verifiable pass/fail signal — not demonstrations (lora-qlora-recipes) or preference pairs (preference-optimization). What follows is when RL is the right tool, the reference. Use it to give an agent explicit responsibilities, steps and constraints.
The full procedure behind SKILL.md's "Judge Calibration Is a Prerequisite" section. Any grader routed to an LLM-judge follows this before its verdicts count toward a pass rate or a checkpoint promotion decision. Use it to give an agent explicit responsibilities, steps and constraints.
This command orchestrates the eval-gated fine-tuning lifecycle across seven phases, each owned by a specialist agent and gated by the artifact the prior phase produced:. Use it to give an agent explicit responsibilities, steps and constraints.
You are the fine-tuning training engineer: the workhorse who takes a training-brief.md someone else already justified and turns it into a dataset, a running job, and an exported artifact. You don't re- litigate method or model choice, and you don't decide whether a checkpoint s. Use it to give an agent explicit responsibilities, steps and constraints.
You are the fine-tuning architect: a skeptical strategist who decides whether fine-tuning is the right tool at all before anyone opens a training config. You are the gate-keeper standing between "the user wants to fine-tune" and the first line of a training script — most requests. Use it to give an agent explicit responsibilities, steps and constraints.
You are an AI engineer specializing in production-grade LLM applications, generative AI systems, and intelligent agent architectures. Use it to give an agent explicit responsibilities, steps and constraints.
logo]: https://github.com/rohan-paul/rohan-paul/blob/master/assets/png. Use it when a task needs concrete terminology, constraints or implementation detail.
A curation of awesome tools, documents and projects about LLM Security. Use it to navigate the topic and choose relevant methods, papers or tools.
Official PyTorch implementation for Extreme Compression of Large Language Models via Additive Quantization. Use it to navigate the topic and choose relevant methods, papers or tools.
Quick Start • Architecture • Training • Validation • Inference • Examples • Configuration • Troubleshooting. Use it to ground design choices in named patterns, trade-offs and examples.
LLM Zoomcamp: Free Course on Building LLM Applications with RAG, Agents & Vector Search Go from LLM basics to a production-ready AI assistant in 10 weeks. Use it to build a structured path from fundamentals to hands-on practice.
MedAlpaca expands upon both Stanford Alpaca and AlpacaLoRA to offer an advanced suite of large language models specifically fine-tuned for medical question-answering and dialogue applications. Our primary objective is to deliver an array of open-source language models, paving the way for seamless development of medical chatbot solutions.
This repo contains the earliest version of the Integuru agent we released publicly. It shows the original approach: using browser network requests to generate runnable integration code for platforms without official APIs.
Reverse-engineered system prompt of Claude Design from Anthropic. A system prompt and skill library that turns an LLM into an opinionated, accessibility-aware, AI-slop-resistant design collaborator.
Get structured, fully typed, and validated JSON outputs from OpenAI and Anthropic models. Under the hood, zod-gpt uses functions to coerce the model to always respond as function calls.
MarkPDFDown English | 中文 | 日本語 | Русский | فارسی | العربية [![Size]][huburl] [![Pulls]][huburl] [![Tag]][tagurl] [![License]][licenseurl] A powerful tool that leverages multimodal large language models to transcribe PDF files into Markdown format. Try our desktop application for a more user-friendly experience!
English | 简体中文 A unified, high-performance framework for training LLMs, VLMs, diffusion, and embodied models. 🌐 Website · 📖 Docs · ✍️ Blog · ⚡ Quick Start · 📊 Performance · 🏛️ Supported Models · 💬 Contact LoongForge is a unified training framework for LLMs, VLMs, diffusion, and embodied models, covering pre-training, continued pre-training, and SFT.
Prompt Decorators extend the functionality of large language models by allowing structured, modular control over reasoning, style, and behavior. Each decorator enforces specific response rules, enabling users to declaratively modify how the model thinks and writes — without retraining.
For embeddings, please see examples/embeddings/index.html WebGPU support is introduced via PR #215. Upon updating to V3.1, WebGPU will be enabled automatically.
This skill assumes finetuning-method-selection already routed here because the data shape is preference pairs or unpaired thumbs-up/down feedback, not demonstrations (that's lora-qlora-recipes) or a verifiable reward signal (that's grpo-rlvr-training). What follows is metho. Use it to give an agent explicit responsibilities, steps and constraints.
This skill assumes finetuning-method-selection already routed here because the target behavior has a verifiable pass/fail signal — not demonstrations (lora-qlora-recipes) or preference pairs (preference-optimization). What follows is when RL is the right tool, the reference. Use it to give an agent explicit responsibilities, steps and constraints.
The full procedure behind SKILL.md's "Judge Calibration Is a Prerequisite" section. Any grader routed to an LLM-judge follows this before its verdicts count toward a pass rate or a checkpoint promotion decision. Use it to give an agent explicit responsibilities, steps and constraints.
This command orchestrates the eval-gated fine-tuning lifecycle across seven phases, each owned by a specialist agent and gated by the artifact the prior phase produced:. Use it to give an agent explicit responsibilities, steps and constraints.
You are the fine-tuning training engineer: the workhorse who takes a training-brief.md someone else already justified and turns it into a dataset, a running job, and an exported artifact. You don't re- litigate method or model choice, and you don't decide whether a checkpoint s. Use it to give an agent explicit responsibilities, steps and constraints.
You are the fine-tuning architect: a skeptical strategist who decides whether fine-tuning is the right tool at all before anyone opens a training config. You are the gate-keeper standing between "the user wants to fine-tune" and the first line of a training script — most requests. Use it to give an agent explicit responsibilities, steps and constraints.
You are an AI engineer specializing in production-grade LLM applications, generative AI systems, and intelligent agent architectures. Use it to give an agent explicit responsibilities, steps and constraints.
logo]: https://github.com/rohan-paul/rohan-paul/blob/master/assets/png. Use it when a task needs concrete terminology, constraints or implementation detail.
A curation of awesome tools, documents and projects about LLM Security. Use it to navigate the topic and choose relevant methods, papers or tools.
Official PyTorch implementation for Extreme Compression of Large Language Models via Additive Quantization. Use it to navigate the topic and choose relevant methods, papers or tools.
Quick Start • Architecture • Training • Validation • Inference • Examples • Configuration • Troubleshooting. Use it to ground design choices in named patterns, trade-offs and examples.
LLM Zoomcamp: Free Course on Building LLM Applications with RAG, Agents & Vector Search Go from LLM basics to a production-ready AI assistant in 10 weeks. Use it to build a structured path from fundamentals to hands-on practice.
MedAlpaca expands upon both Stanford Alpaca and AlpacaLoRA to offer an advanced suite of large language models specifically fine-tuned for medical question-answering and dialogue applications. Our primary objective is to deliver an array of open-source language models, paving the way for seamless development of medical chatbot solutions.
This repo contains the earliest version of the Integuru agent we released publicly. It shows the original approach: using browser network requests to generate runnable integration code for platforms without official APIs.
Reverse-engineered system prompt of Claude Design from Anthropic. A system prompt and skill library that turns an LLM into an opinionated, accessibility-aware, AI-slop-resistant design collaborator.
Get structured, fully typed, and validated JSON outputs from OpenAI and Anthropic models. Under the hood, zod-gpt uses functions to coerce the model to always respond as function calls.
MarkPDFDown English | 中文 | 日本語 | Русский | فارسی | العربية [![Size]][huburl] [![Pulls]][huburl] [![Tag]][tagurl] [![License]][licenseurl] A powerful tool that leverages multimodal large language models to transcribe PDF files into Markdown format. Try our desktop application for a more user-friendly experience!
English | 简体中文 A unified, high-performance framework for training LLMs, VLMs, diffusion, and embodied models. 🌐 Website · 📖 Docs · ✍️ Blog · ⚡ Quick Start · 📊 Performance · 🏛️ Supported Models · 💬 Contact LoongForge is a unified training framework for LLMs, VLMs, diffusion, and embodied models, covering pre-training, continued pre-training, and SFT.
Prompt Decorators extend the functionality of large language models by allowing structured, modular control over reasoning, style, and behavior. Each decorator enforces specific response rules, enabling users to declaratively modify how the model thinks and writes — without retraining.
For embeddings, please see examples/embeddings/index.html WebGPU support is introduced via PR #215. Upon updating to V3.1, WebGPU will be enabled automatically.