Chatbot Arena

This leaderboard is based on the following three benchmarks. Chatbot Arena - a crowdsourced, randomized battle platform for large language models (LLMs). We use 1.5M+ user votes to compute Elo ratings. MT-Bench - a set of challenging multi-turn questions. We use GPT-4 to grade model responses. MMLU (5-shot) - a test to measure a model’s multitask accuracy on 57 tasks.

DeepSeek-Coder-V2

We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks.

Qwen 2

After months of efforts, we are pleased to announce the evolution from Qwen1.5 to Qwen2. This time, we bring to you: Pretrained and instruction-tuned models of 5 sizes, including Qwen2-0.5B, Qwen2-1.5B, Qwen2-7B, Qwen2-57B-A14B, and Qwen2-72B; Having been trained on data in 27 additional languages besides English and Chinese; State-of-the-art performance in a large number of benchmark evaluations; Significantly improved performance in coding and mathematics; Extended context length support up to 128K tokens with Qwen2-7B-Instruct and Qwen2-72B-Instruct.

Coder EvalPlus

EvalPlus is a rigorous evaluation framework for LLM4Code, with: ✨ HumanEval+: 80x more tests than the original HumanEval! ✨ MBPP+: 35x more tests than the original MBPP! ✨ Evaluation framework: our packages/images/tools can easily and safely evaluate LLMs on above benchmarks. File a request to add your models on our leaderboard!

DeepSeek-V2

We introduce DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to more than 5 times.

Text2SQL Leaderboard

Text-to-SQL (or Text2SQL), as the name implies, is to convert text into SQL. A more academic definition is to convert natural language problems in the database field into structured query languages ​​that can be executed in relational databases. Therefore, Text-to-SQL can also be abbreviated as NL2SQL. Input: natural language questions, such as Query the relevant information of the table t_user, and the results are sorted in descending order by id.

Llama 3

Today, we’re excited to share the first two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases. This next generation of Llama demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning.

LLM Course

The LLM course is divided into three parts: 🧩 LLM Fundamentals covers essential knowledge about mathematics, Python, and neural networks. 🧑‍🔬 The LLM Scientist focuses on building the best possible LLMs using the latest techniques. 👷 The LLM Engineer focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two LLM assistants that will answer questions and test your knowledge in a personalized way: