WizardLM
@WizardLM_AI
WizardLM, WizardCoder, WizardMath. Evol-Instruct, Arena Learning, RLEIF.
Anda mungkin suka
🎉Today we are announcing Evol-Instruct V2 !!! 🔥 Auto Evol-Instruct is one of the most important technologies for WizardLM-2. Paper link: arxiv.org/pdf/2406.00770 We build a fully automated Evol-Instruct pipeline, allowing WizardLM-2 to be extended from three evolved domains…
🔥 Excited to share the other key Technology of WizardLM-2! 📙AutoEvol: Automatic Instruction Evolving for Large Language Models 🚀We build a fully automated Evol-Instruct pipeline to create high-quality, highly complex instruction tuning data: -------- 🧵 --------…
🎉 Thrilled to share our paper accepted by #KDD2025! 🌟AgentGen🌟: An automated environment and task generator that enhances LLM-based agents' planning abilities through diverse, difficulty-controlled synthetic trajectory data. 👇🏻agent-gen.github.io
🚀New approach from WaveCoder Team for optimizing code LLMs. The novel feature tree based framework, inspired by AST and Evol-Instruct to modeling semantic relationships, generates more diverse data. The EpiCoder hits SOTA in both challenge file and function benchmarks.
🚀 Introducing EpiCoder: a hierarchical feature tree-based framework for diverse and intricate code generation. 🔍 Outperforming benchmarks, it handles everything from simple functions to multi-file projects deftly. 📢 Open source release soon! 🔗 arxiv.org/abs/2501.04694
GENIAC phase2にて、日本語のローカルLLMを使ってEvol-Instructによるデータセット構築に取り組んだ際の記事を公開しました。(3件目/全4件) zenn.dev/matsuolab/arti…
Agentic Information Retrieval This paper provides a good introduction to agentic information retrieval, which is shaped by the capabilities of LLM agents. I've been developing with this paradigm recently and it does offer lots of interesting ways to optimize retrieval systems.
🎉 x 2
🎉Excited to see the core techniques of WizardLM2 has been accepted to #EMNLP2024 and #NeurIPS2024! 👉Automatic Instruction Evolving for Large Language Models (arxiv.org/abs/2406.00770) 👉Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arena…
Congrats! We are dedicated to innovating synthetic training techniques, drawing inspiration from theory of evolution. The previous Evol-Instruct focused on evolving higher-value instructions from the instruction side. This work, Arena Learning, emphasizes the evolution of…
🎉Excited to see our WizardArena paper accepted to #NeurIPS2024! 👉Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arena (arxiv.org/abs/2407.10627) Congrats to all outstanding co-authors. We will not stop our journey towards AGI.
We are open sourcing our EvolKit pipeline that was instrumental in the creation of supernova, under MIT license. This was heavily inspired by the AutoEvol paper from @WizardLM_AI, and is a tremendously powerful tool for creating complex datasets. Find it here:…
🚀 Excited to introduce MMEvol, which improves MLLM through complex and diverse instruction with perceptual, cognitive, and interactive evolution. 🌟 Achieving a 3.1% accuracy boost across 13 VL tasks. Code and data will be released soon; stay tuned! 📄 huggingface.co/papers/2409.05…
Impressed by @arcee_ai team's work. Proud to open-source EvolKit: framework for evolving instruction data with OPEN-SOURCE models. Inspired by @WizardLM_AI, result of my month-long effort. GitHub: github.com/arcee-ai/EvolK… Don't forget SuperNova too! 🥳
We are announcing Llama-3.1-SuperNova, a Llama-3.1-70B-Instruct model offline distilled from Llama-3.1-405B-Instruct. It's ridiculously strong, particularly in instruction following and math. It's available to play with at supernova.arcee.ai. Read more about the model and…
Learn what’s next for AI at Research Forum on Sept. 3; WizardArena simulates human-annotated chatbot games; MInference speeds pre-filling for long-context LLMs via dynamic sparse attention; Reef: Fast succinct non-interactive zero-knowledge regex proofs. msft.it/6019l4Qv9
Congrats this impressive contribution to OSS community! Also excited to see the state-of-the-art Hermes-3 models also leverage our Evol-Instruct to empower their complex instruction following capacities.
Introducing 𝐇𝐞𝐫𝐦𝐞𝐬 𝟑: The latest version in our Hermes series, a generalist language model 𝐚𝐥𝐢𝐠𝐧𝐞𝐝 𝐭𝐨 𝐲𝐨𝐮. nousresearch.com/hermes3/ Hermes 3 is available in 3 sizes, 8, 70, and 405B parameters. Hermes has improvements across the board, but with particular…
-- <big_model_smell> benchmark -- Aidan Bench measures creativity, reliability, attention, and instruction following. >mistral large 2 wins by a lot??? >gpt-4o sucks confirmed >sonnet-3.5 remains very strong >gpt-4-0314 shows old man strength github.com/aidanmclaughli…
The @WizardLM_AI team is back, with Evol-Instruct V2 (actually more like "Auto" Evol-Instruct), which is one of core components for WizardLM-2 Thank you @WizardLM_AI team.
🎉Today we are announcing Evol-Instruct V2 !!! 🔥 Auto Evol-Instruct is one of the most important technologies for WizardLM-2. Paper link: arxiv.org/pdf/2406.00770 We build a fully automated Evol-Instruct pipeline, allowing WizardLM-2 to be extended from three evolved domains…
Exciting! Evol Instruct v2 paper is out! Microsoft Research WizardLM just released Auto-Evol-Instruct! (I consider this type of work pre/proto-agi, but my views are sometimes radical) arxiv.org/pdf/2406.00770
Don't sleep! WizardLM just dropped the (probably) best data generation method ever known to mankind! I didn't get to read it all out yet (later on today), but from what Iv'e seen the main ideas are: 1. Ask a model to "make the task harder" (WizardLM-1) 2. BUT also ask a model…
🔥 Excited to share the other key Technology of WizardLM-2! 📙AutoEvol: Automatic Instruction Evolving for Large Language Models 🚀We build a fully automated Evol-Instruct pipeline to create high-quality, highly complex instruction tuning data: -------- 🧵 --------…
A synthetic data pipeline for making LLM training data. Neat. 🧙♂️✨
🎉Today we are announcing Evol-Instruct V2 !!! 🔥 Auto Evol-Instruct is one of the most important technologies for WizardLM-2. Paper link: arxiv.org/pdf/2406.00770 We build a fully automated Evol-Instruct pipeline, allowing WizardLM-2 to be extended from three evolved domains…
These technologies for Improving Data Training Pipeline from @WizardLM_AI will benefit all LLMs. Thank you! I am hoping there will be big increase in creative writing scores with this method!
🔥 Excited to share the other key Technology of WizardLM-2! 📙AutoEvol: Automatic Instruction Evolving for Large Language Models 🚀We build a fully automated Evol-Instruct pipeline to create high-quality, highly complex instruction tuning data: -------- 🧵 --------…
If you’ve spoken to me since the official announcement of WizardLM-2 in April, there’s a 99% chance I was in your ear rambling about how much I was looking forward to the paper detailing their new training data synthesis pipeline - today is my Christmas Thank you @WizardLM_AI…
🎉Today we are announcing Evol-Instruct V2 !!! 🔥 Auto Evol-Instruct is one of the most important technologies for WizardLM-2. Paper link: arxiv.org/pdf/2406.00770 We build a fully automated Evol-Instruct pipeline, allowing WizardLM-2 to be extended from three evolved domains…
wow🔥 it’s the upgrade version from Evol-Instruct!
🔥 Excited to share the other key Technology of WizardLM-2! 📙AutoEvol: Automatic Instruction Evolving for Large Language Models 🚀We build a fully automated Evol-Instruct pipeline to create high-quality, highly complex instruction tuning data: -------- 🧵 --------…
❤️Thanks Wavecoder brother.
(1/3) Quite interesting work！Evol-Instruct has recently undergone a significant update and upgrade, introducing the brand-new automatic evolutionary framework, Auto Evol.
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