🚨 How green is your model? 🌱 Introducing a new feature in the Comparator tool: Environmental Impact for responsible #LLM research! 👉 open-llm-leaderboard/comparator Now, you can not only compare models by performance, but also by their environmental footprint!
🌍 The Comparator calculates CO₂ emissions during evaluation and shows key model characteristics: evaluation score, number of parameters, architecture, precision, type... 🛠️ Make informed decisions about your model's impact on the planet and join the movement towards greener AI!
🚀 New feature of the Comparator of the 🤗 Open LLM Leaderboard: now compare models with their base versions & derivatives (finetunes, adapters, etc.). Perfect for tracking how adjustments affect performance & seeing innovations in action. Dive deeper into the leaderboard!
🛠️ Here's how to use it: 1. Select your model from the leaderboard. 2. Load its model tree. 3. Choose any base & derived models (adapters, finetunes, merges, quantizations) for comparison. 4. Press Load. See side-by-side performance metrics instantly!
Ready to dive in? 🏆 Try the 🤗 Open LLM Leaderboard Comparator now! See how models stack up against their base versions and derivatives to understand fine-tuning and other adjustments. Easier model analysis for better insights! Check it out here: open-llm-leaderboard/comparator 🌐
Dive into multi-model evaluations, pinpoint the best model for your needs, and explore insights across top open LLMs all in one place. Ready to level up your model comparison game?
🚨 Instruct-tuning impacts models differently across families! Qwen2.5-72B-Instruct excels on IFEval but struggles with MATH-Hard, while Llama-3.1-70B-Instruct avoids MATH performance loss! Why? Can they follow the format in examples? 📊 Compare models: open-llm-leaderboard/comparator
Need an LLM assistant but unsure which hashtag#smolLM to run locally? With so many models available, how can you decide which one suits your needs best? 🤔
If the model you’re interested in is evaluated on the Hugging Face Open LLM Leaderboard, there’s an easy way to compare them: use the model Comparator tool: open-llm-leaderboard/comparator Let’s walk through an example👇
Let’s compare two solid options: - Qwen2.5-1.5B-Instruct from Alibaba Cloud Qwen (1.5B params) - gemma-2-2b-it from Google (2.5B params)
For an assistant, you want a model that’s great at instruction following. So, how do these two models stack up on the IFEval task?
What about other evaluations? Both models are close in performance on many other tasks, showing minimal differences. Surprisingly, the 1.5B Qwen model performs just as well as the 2.5B Gemma in many areas, even though it's smaller in size! 📊
This is a great example of how parameter size isn’t everything. With efficient design and training, a smaller model like Qwen2.5-1.5B can match or even surpass larger models in certain tasks.
Looking for other comparisons? Drop your model suggestions below! 👇
🚨 We’ve just released a new tool to compare the performance of models in the 🤗 Open LLM Leaderboard: the Comparator 🎉 open-llm-leaderboard/comparator
Want to see how two different versions of LLaMA stack up? Let’s walk through a step-by-step comparison of LLaMA-3.1 and LLaMA-3.2. 🦙🧵👇
1/ Load the Models' Results - Go to the 🤗 Open LLM Leaderboard Comparator: open-llm-leaderboard/comparator - Search for "LLaMA-3.1" and "LLaMA-3.2" in the model dropdowns. - Press the Load button. Ready to dive into the results!
2/ Compare Metric Results in the Results Tab 📊 - Head over to the Results tab. - Here, you’ll see the performance metrics for each model, beautifully color-coded using a gradient to highlight performance differences: greener is better! 🌟 - Want to focus on a specific task? Use the Task filter to hone in on comparisons for tasks like BBH or MMLU-Pro.
3/ Check Config Alignment in the Configs Tab ⚙️ - To ensure you’re comparing apples to apples, head to the Configs tab. - Review both models’ evaluation configurations, such as metrics, datasets, prompts, few-shot configs... - If something looks off, it’s good to know before drawing conclusions! ✅
4/ Compare Predictions by Sample in the Details Tab 🔍 - Curious about how each model responds to specific inputs? The Details tab is your go-to! - Select a Task (e.g., MuSR) and then a Subtask (e.g., Murder Mystery) and then press the Load Details button. - Check out the side-by-side predictions and dive into the nuances of each model’s outputs.
5/ With this tool, it’s never been easier to explore how small changes between model versions affect performance on a wide range of tasks. Whether you’re a researcher or enthusiast, you can instantly visualize improvements and dive into detailed comparisons.
🚀 Try the 🤗 Open LLM Leaderboard Comparator now and take your model evaluations to the next level!
Recently, the Hugging Face 🤗 datasets team met with the Language Technologies team led by Marta Villegas (@mvillegas) at Barcelona Supercomputing Center @BSC-LT. Eager to collaborate to promote AI across Catalan, Spanish, Basque, and Galician languages and share open-source datasets/models. 🤝 #AI #LanguageTech #OpenSource
🔥 What's New: - Polars integration 🐻❄️ - fsspec support for conversion to JSON, CSV, and Parquet - Mode parameter for Image feature - CLI function to convert script-datasets to Parquet - Dataset.take and Dataset.skip
Plus, a bunch of general improvements & bug fixes!
🚀 Just released version 0.23.0 of the huggingface_hub Python library!
Exciting updates include: 📁 Seamless download to local dir! 💡 Grammar and Tools in InferenceClient! 🌐 Documentation full translated to Korean! 👥 User API: get likes, upvotes, nb of repos, etc.! 🧩 Better model cards and encoding for ModelHubMixin!
