AI and Machine Learning

🌟 Llama3-SWE-RL: A Methodology for Teaching LLM for Software Development Tasks Using RL. SWE-RL is a reinforcement learning technique for software engineering tasks using data from open source Github repositories. Llama3-SWE-RL imparts reasoning skills by improving results on tasks outside the general coding domain: functional programming, library usage, code planning, mathematical operations, and NLP. Unlike SFT, SWE-RL allows the model to improve its general reasoning abilities. The methodology pipeline consists of a sequence of stages: 🟢 The first stage is collecting, moderating and aggregating pull requests from public Github repositories, marking and converting this array into a dataset (problem description-code context - "oracle patch")

Oracle patch is a benchmark code patch used to train and evaluate language models in tasks related to automated software problem solving.

🟢 Second stage: training LLM to generate code based on task and context, calculating reward for RL (here they use similarity score between model inference and "oracle patch" using difflib.SequenceMatcher. Incorrect answers get negative reward) 🟢 Stage three: adjusting and optimizing the training policy using GPRO. The Llama3-SWE-RL-70B benchmark model, trained on Llama-3.3-70B-Instruct using SWE-RL, achieved 41.0% solve rate on SWE-bench Verified, which is the best among medium-sized models (<100B) and comparable to the result of GPT-4o. An implementation of SWE-RL is available in the project repository , where the developers provide prompt templates and an implementation of the sequence similarity reward function . ▶️ Local installation with an example of use in a project:

 # Install SWE-RL
git clone https://github.com/facebookresearch/swe-rl && cd swe-rl
pip install -e ".[dev]"
pytest

# example on how you can use the reward function in your own project:
import swerl

file = """
def sort_list(lst):
return sorted(lst)
""".strip()

oracle_file = """
def sort_list(lst: list[int]) -> list[int]:
return sorted(lst)
""".strip()

context = {"example.py": file}
oracle = {"example.py": oracle_file}

output = """
<think>
...thoughts by LLM
</think>
<solution>
```python
### example.py
<<<<<<< SEARCH
def sort_list(lst):
=======
def sort_list(lst: list[int]) -> list[int]:
>>>>>>> REPLACE
</solution>
""".strip()

reward, metadata = swerl.core.reward.calculate_search_replace_reward(context, oracle, output)
assert reward == 1.0
print(metadata)

📌 Licensing: CC-NC-4.0 License. 🟡 Arxiv 🖥 GitHub

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✔️ DeepSeek Open Source Week continues! The Chinese have just introduced DeepEP , a library designed to optimize the performance of models with Mixture-of-Experts (MoE) architecture and Expert Parallelism ( EP ). Its main goal is to provide high throughput and low latency for data exchange between GPUs , which is critical for efficient training and inference of large models. What's inside High performance: - The library provides optimized all-to-all GPU cores for data dispatch and combine operations, which improves the speed and efficiency of communication between experts in the model. - DeepEP supports low-precision operations , including FP8 format, which helps reduce memory requirements and increase computation speed without significant loss of precision. - Optimization for different domains: In line with the group constrained gating algorithm proposed in DeepSeek-V3, the library offers a set of cores optimized for asymmetric data transfer between different domains, such as NVLink and RDMA. This ensures high throughput in training and inference. - Low Latency for Inference : For latency-sensitive tasks, DeepEP includes a set of pure RDMA cores, minimizing latency and ensuring fast data processing during inference. - Works with both NVLink and RDMA, allowing for high-performance communication between GPUs both within a single server and between different servers. Operating principle: DeepEP integrates into existing MoE model training and inference workflows by providing efficient mechanisms for inter-GPU data exchange. Using optimized communication cores, the library ensures fast and reliable data transfer, which is especially important when working with large models and distributed systems. Support for low-precision operations and optimization for different domains allows for flexible tuning of the system to specific requirements and hardware capabilities. Using DeepEP helps improve the efficiency and performance of MoE models, making them easier to scale and accelerating training and inference processes. ▪️ Github

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Many data scientists don't know how to push ML models to production. Here's the recipe 👇 𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀 🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data 🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time 🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model 🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github 🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API 🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API 🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker 🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs 🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲 𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling. 𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility. 𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment 𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker. 𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.

LLM Project Ideas 👆

🔗 Machine Learning libraries

🔰 🔗 Web scraping will never be the same

Crawl4AI simplifies web crawling and data extraction, making it ready to use for LLMs and AI applications.

Here’s why it’s a game-changer: 🆓 Free and open-source ⭐️ Blazing fast performance, 🤖 LLM-friendly output formats (JSON, cleaned HTML, markdown) 🌍 Supports crawling multiple URLs simultaneously 🎨 Extracts all media tags (Images, Audio, Video) 🔗 Extracts all external and internal links But that’s not all: 📚 Extracts metadata from pages ✅ User-agent customization ✅ Takes screenshots of pages 📄 Executes custom JavaScript before crawling

Finally Europe begins the understand that AI is more than just a trend - its a technological revolution and necessity if we want to stay on top as a nation. The only two questions are: - Are we too late or do we still have a chance to catch up? - And also how exactly we want to participate. A separate frontier model will probably be unnecessary. AI infrastructure and hyperscalers in the EU, on the other hand, will be necessary

Bill Gates warns young people of four major global threats, including AI In a recent interview, Bill Gates warned young people about four major global threats: climate change, bioterrorism or pandemics, the risk of nuclear war, and unchecked artificial intelligence (AI). While he acknowledges that concerns about nuclear war persist, he emphasizes that younger generations must also contend with the potential dangers of advanced AI, which could outsmart humans and pose existential risks. Gates is not against AI; he believes it can be beneficial, particularly in addressing skill shortages. Despite these threats, he remains optimistic about the future, predicting advancements in healthcare and innovation that could significantly improve global conditions. Gates encourages the younger generation to take action to mitigate these risks.

🔅 Feature Encoding 101: Prepare Data For Machine Learning

Today we learn about various feature encoding methods. These are important in order to turn all sorts of features into meaningful numerical representations.

Have you ever seen a Drone working under a waterway

Mastering LLM & Generative AI ✅

Machine Learning Cheatsheet 💪

🌟 ReasonFlux: Mathematical Reasoning for LLM. ReasonFlux is a technique used for both training and inference to improve LLM's ability to perform complex logical reasoning. It outperforms OpenAI o1-preview and DeepSeek V3 in mathematical reasoning tasks. When used in training, ReasonFlux uses a hierarchical structure with reinforcement on a sequence of high-level thinking patterns. This allows the basic LLM to learn to plan an optimal pattern trajectory for solving complex problems. During training, ReasonFlux analyzes and summarizes problem-solving information, identifying common patterns, and creates thinking patterns based on this. During inference, ReasonFlux automatically extracts relevant thinking patterns and scales them to achieve superior performance on complex reasoning tasks. It dynamically selects the most relevant high-level pattern for each subtask, simplifying the search for reasoning paths. ReasonFlux uses a new scaling system during inference that adapts thinking patterns. In experimental tests, ReasonFlux-32B achieved 91.2% accuracy on the MATH benchmark, beating o1-preview by 6.7%. On the AIME benchmark, the model solved 56.7% of problems on average, outperforming o1-preview and DeepSeek-V3 by 27% and 45%, respectively. The practical implementation of the method is available in the project repository , it contains the necessary code and description for the LLM fine-tune using the example of the SFT dataset of GaoKao Bench solutions. ⚠️ The project uses the LLaMA-Factory framework to train models on the SFT network. ▶️ Local installation and launch:

 # Clone the repository
git clone https://github.com/ReasonFlux
cd ReasonFlux

# Create a Conda venv
conda create -n ReasonFlux python==3.9
conda activate ReasonFlux

# Install dependencies
pip install -r requirements.txt

# When you complete your first-stage training, you can try to use simple inference
from reasonflux import ReasonFlux

reasonflux = ReasonFlux(navigator_path='path-to-navigator',
template_matcher_path='jinaai/jina-embeddings-v3',
inference_path='path-to-infernece-model',
template_path='template_library.json')
problem = """Given a sequence {aₙ} satisfying a₁=3, and aₙ₊₁=2aₙ+5 (n≥1), find the general term formula aₙ"""

📌 Licensing: Apache 2.0 License. 🟡 Arxiv 🟡 SFT Dataset (in Chinese) 🖥 GitHub

📌 A practical guide to the pitfalls of large language models with examples. An open preprint of a book by Tharsis Souza, PhD, University of London, which provides a critical analysis of the challenges and limitations faced by engineers and technical project managers when developing LLM-based applications. The goal of the book, according to the author, is to help create reliable and secure systems based on LLM, avoiding common mistakes. It is aimed at developers, technical project managers, and technical leaders seeking to gain a deeper understanding and overcome the practical challenges associated with implementing LLM. In contrast to the prevailing discourse that emphasizes the possibilities of LLM, the book focuses on the practical difficulties and potential implementation pitfalls, offering detailed guidance on how to overcome them. The book covers issues of structural unreliability, input data management, testing, security and alignment aspects, vendor dependency, and cost optimization. The book is accompanied by a repository with practical examples in Python, analysis of real scenarios and solutions. ▶️ Content: 🟢 Preface 🟢 About the book 🟢 Chapter 1: Gaps in Assessment 🟢 Chapter 2: Structured Output 🟢 Chapter 3: Input Data Management 🟢 Chapter 4: Security 🟢 Chapter 5: Preference-Based Alignment 🟢 Chapter 6: Local Models in Practice 🟠 Chapter 7: The Paradox of Cost Reduction (unpublished) 🟠 Chapter 8: Boundaries (Unpublished) 🟠 Appendix: Tools and Resources (unpublished) 🟡 Project page 🖥 Github.com