Jointly announcing EAGLE-3 with SGLang: Setting a new record in LLM inference acceleration!

- 5x🚀than vanilla (on HF)
- 1.4x🚀than EAGLE-2 (on HF)
- A record of ~400 TPS on LLama 3.1 8B with a single H100 (on SGLang)
- 1.65x🚀in latency even for large bs=64 (on SGLang)
- A new scaling law: more training data, better speedup
- Apache 2.0

Paper: https://arxiv.org/abs/2503.01840
Code: https://github.com/SafeAILab/EAGLE
SGLang version: https://github.com/sgl-project/sglang/pull/4247

@Machine_learn

Executable Code Actions Elicit Better LLM Agents

1 Feb 2024 · Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji

Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking tools and controlling robots, show great potential in tackling real-world challenges. LLM agents are typically prompted to produce actions by generating #JSON or text in a pre-defined format, which is usually limited by constrained action space (e.g., the scope of pre-defined tools) and restricted flexibility (e.g., inability to compose multiple tools). This work proposes to use executable Python code to consolidate LLM agents' actions into a unified action space (CodeAct). Integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions. Our extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark shows that CodeAct outperforms widely used alternatives (up to 20% higher success rate). The encouraging performance of CodeAct motivates us to build an open-source #LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language. To this end, we collect an instruction-tuning dataset CodeActInstruct that consists of 7k multi-turn interactions using CodeAct. We show that it can be used with existing data to improve models in agent-oriented tasks without compromising their general capability. CodeActAgent, finetuned from Llama2 and Mistral, is integrated with #Python interpreter and uniquely tailored to perform sophisticated tasks (e.g., model training) using existing libraries and autonomously self-debug.

Paper: https://arxiv.org/pdf/2402.01030v4.pdf

Codes:
https://github.com/epfllm/megatron-llm
https://github.com/xingyaoww/code-act

Datasets: MMLU - GSM8K - HumanEval - MATH

@Machine_learn

Introduction to Graph Neural Networks: A Starting Point for Machine Learning Engineers

📓 Paper


@Machine_learn

تنها نفر ۲ و ۳ از این باقی موندن....!

Applied Generative AI for Beginners

@Machine_learn

Graph Theory and Additive Combinatorics
Exploring Structure and Randomness

📚 link


@Machine_learn

Bias-Variance Trade-Off in Statistics at MIT OpenCourseWare

📚 Book



@Machine_learn

Greetings.
As part of our research, we want to write a review article in the field of pathology. Friends who are interested in the 2nd and 3rd places on this topic can participate.

✅ Approximate start time: April 10th.

Journal: scientific reports https://www.nature.com/srep/

Price:
2: $400
3: $300

I will help with complete explanations and how to write each section.

@Raminmousa
@Machine_learn
@Paper4money