Fine Tuning LLMs with Hugging Face LLMs Code

https://t.iss.one/deep_learning_proj

Foundations of Large Language Models

📝 Table of Contents:
● Pre-training
● Generative Models
● Prompting
● Alignment

Tong Xiao and Jingbo Zhu
January 17, 2025

📃 Download from arXiv.

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Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap

Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence. The interplay between LLMs and evolutionary algorithms (EAs), despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM's further enhancement under black-box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this paper provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: LLM-enhanced EA and EA-enhanced #LLM. Some integrated synergy methods are further introduced to exemplify the complementarity between LLMs and EAs in diverse scenarios, including code generation, software engineering, neural architecture search, and various generation tasks. As the first comprehensive review focused on the EA research in the era of #LLMs, this paper provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. The identified challenges and future directions offer guidance for researchers and practitioners to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence.

Paper: https://arxiv.org/pdf/2401.10034v3.pdf

Code: https://github.com/wuxingyu-ai/llm4ec

https://t.iss.one/deep_learning_proj

How to run 🐋 DeepSeek locally on your Computer

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🐫Tülu 3 (what a name) 405B - ​​another release!

An open source model (and no, it's not a Chinese model) that outperforms the DeepSeek-V3! on multiple benchmarks

Scalable to 405B - ​​with performance on par with GPT-4o and outperforming previous models in the same class.

▪ Blog: https://allenai.org/blog/tulu-3-405B
▪You can test it here: https://playground.allenai.org/?model=tulu3-405b
▪ Technical report: https://allenai.org/blog/tulu-3-technical
▪ Hugging Face : https://huggingface.co/collections/allenai/tulu-3-models-673b8e0dc3512e30e7dc54f5

https://t.iss.one/deep_learning_proj

⚡ LitGPT



pip install 'litgpt[all]'



from litgpt import LLM

llm = LLM.load("microsoft/phi-2")
text = llm.generate("Fix the spelling: Every fall, the familly goes to the mountains.")
print(text)
# Corrected Sentence: Every fall, the family goes to the mountains.

▪Github
▪Docs
▪Video

https://t.iss.one/deep_learning_proj

RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation

5 Aug 2024 · Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat, Peter Izsak ·

Implementing Retrieval-Augmented Generation (RAG) systems is inherently complex, requiring deep understanding of data, use cases, and intricate design decisions. Additionally, evaluating these systems presents significant challenges, necessitating assessment of both retrieval accuracy and generative quality through a multi-faceted approach. We introduce RAG Foundry, an open-source framework for augmenting large language models for RAG use cases. RAG Foundry integrates data creation, training, inference and evaluation into a single workflow, facilitating the creation of data-augmented datasets for training and evaluating large language models in RAG settings. This integration enables rapid prototyping and experimentation with various RAG techniques, allowing users to easily generate datasets and train RAG models using internal or specialized knowledge sources. We demonstrate the framework effectiveness by augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG configurations, showcasing consistent improvements across three knowledge-intensive datasets.

Paper: https://arxiv.org/pdf/2408.02545v1.pdf

Code: https://github.com/intellabs/ragfoundry

Datasets: TriviaQA - PubMedQA

https://t.iss.one/deep_learning_proj

FireRedASR: Open-Source Industrial-Grade Mandarin Speech Recognition Models from Encoder-Decoder to LLM Integration

24 Jan 2025 · Kai-Tuo Xu, Feng-Long Xie, Xu Tang, Yao Hu ·

We present FireRedASR, a family of large-scale automatic speech recognition (ASR) models for Mandarin, designed to meet diverse requirements in superior performance and optimal efficiency across various applications. FireRedASR comprises two variants: FireRedASR-LLM: Designed to achieve state-of-the-art (SOTA) performance and to enable seamless end-to-end speech interaction. It adopts an Encoder-Adapter-LLM framework leveraging large language model (LLM) capabilities. On public Mandarin benchmarks, FireRedASR-LLM (8.3B parameters) achieves an average Character Error Rate (CER) of 3.05%, surpassing the latest SOTA of 3.33% with an 8.4% relative CER reduction (CERR). It demonstrates superior generalization capability over industrial-grade baselines, achieving 24%-40% CERR in multi-source Mandarin ASR scenarios such as video, live, and intelligent assistant. FireRedASR-AED: Designed to balance high performance and computational efficiency and to serve as an effective speech representation module in LLM-based speech models. It utilizes an Attention-based Encoder-Decoder (AED) architecture. On public Mandarin benchmarks, FireRedASR-AED (1.1B parameters) achieves an average CER of 3.18%, slightly worse than FireRedASR-LLM but still outperforming the latest SOTA model with over 12B parameters. It offers a more compact size, making it suitable for resource-constrained applications. Moreover, both models exhibit competitive results on Chinese dialects and English speech benchmarks and excel in singing lyrics recognition.

Paper: https://arxiv.org/pdf/2501.14350v1.pdf

Code: https://github.com/fireredteam/fireredasr

Datasets: LibriSpeech - AISHELL-1 - AISHELL-2 - WenetSpeech

https://t.iss.one/deep_learning_proj

Tutorial: Train your own Reasoning model with GRPO

📓 Tutorial

https://t.iss.one/deep_learning_proj

Slamming: Training a Speech Language Model on One GPU in a Day

19 Feb 2025 · Gallil Maimon, Avishai Elmakies, Yossi Adi ·

We introduce Slam, a recipe for training high-quality Speech Language Models (SLMs) on a single academic GPU in 24 hours. We do so through empirical analysis of model initialisation and architecture, synthetic training data, preference optimisation with synthetic data and tweaking all other components. We empirically demonstrate that this training recipe also scales well with more compute getting results on par with leading SLMs in a fraction of the compute cost. We hope these insights will make SLM training and research more accessible. In the context of SLM scaling laws, our results far outperform predicted compute optimal performance, giving an optimistic view to #SLM feasibility. See code, data, models, samples at - https://pages.cs.huji.ac.il/adiyoss-lab/slamming .

Paper: https://arxiv.org/pdf/2502.15814v1.pdf

Code: https://github.com/slp-rl/slamkit



https://t.iss.one/deep_learning_proj

رمضان الکریم ❤️
@Machine_learn