Stories with Tag Language Model Training

• Teuken-7B-Base and Teuken-7B-Instruct: Towards European LLMs

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  Posted: 2025-04-15 10:17:17

  Verbose tl;dr

  Researchers introduce Teukten-7B, a new family of 7-billion parameter language models specifically trained on a diverse European dataset. The models, Teukten-7B-Base and Teukten-7B-Instruct, aim to address the underrepresentation of European languages and cultures in existing LLMs. Teukten-7B-Base is a general-purpose model, while Teukten-7B-Instruct is fine-tuned for instruction following. The models are pre-trained on a multilingual dataset heavily weighted towards European languages and demonstrate competitive performance compared to existing models of similar size, especially on European-centric benchmarks and tasks. The researchers emphasize the importance of developing LLMs rooted in diverse cultural contexts and release Teukten-7B under a permissive license to foster further research and development within the European AI community.

  The preprint "Teuken-7B-Base and Teuken-7B-Instruct: Towards European LLMs" introduces two new open-source large language models (LLMs) named Teuk-7B-Base and Teuk-7B-Instruct, developed with a focus on European languages and data privacy. The authors argue for the importance of developing LLMs within Europe to address specific regional needs, maintain data sovereignty, and foster a robust European AI ecosystem. They highlight the risks associated with relying solely on LLMs trained outside the region, particularly concerning data privacy and potential biases reflecting values and cultural norms different from European ones.

  Teuken-7B-Base serves as the foundational model, pre-trained on a diverse multilingual dataset curated with an emphasis on European languages. This dataset, known as "EuroMix-4B," is comprised of text and code drawn from various sources, including Common Crawl, Europarl, and publicly accessible code repositories. The authors detail the data processing pipeline, including filtering for quality, deduplication, and language identification. They also emphasize their focus on data privacy by exclusively using publicly available data and minimizing the inclusion of personally identifiable information (PII).

  Built upon Teuken-7B-Base, Teuken-7B-Instruct is further refined through supervised fine-tuning (SFT) to better align with user instructions and generate more relevant and helpful responses. This fine-tuning process leverages a dataset derived from publicly available instruction datasets translated and augmented for improved performance across European languages. The authors explain the specific techniques used for instruction tuning, including data formatting and optimization strategies.

  The paper presents a comprehensive evaluation of both Teuken-7B-Base and Teuken-7B-Instruct, benchmarking their performance against other existing LLMs across a variety of tasks. These evaluations include standard language modeling benchmarks, as well as specific tests designed to assess their understanding of European languages and cultural contexts. The results demonstrate competitive performance across several metrics, suggesting the efficacy of the proposed training methodology and the value of specializing LLMs for specific regional needs.

  Furthermore, the authors emphasize the open-source nature of both models and the associated training data, aiming to promote transparency and facilitate further research and development within the European AI community. They also highlight the potential applications of these models in various domains, ranging from content generation and translation to code completion and customer service. Finally, the paper concludes by outlining future research directions, including scaling up the model size, expanding the training data to encompass more languages and cultural contexts, and exploring further advancements in fine-tuning techniques to further improve the models' capabilities and their alignment with user expectations.

  • Large Language Models
  • LLMs
  • natural language processing
  • NLP
  • machine learning
  • AI
  • artificial intelligence
  • deep learning
  • European
  • Teuken
  • Teuken-7B-Base
  • Teuken-7B-Instruct
  • Language Model Training
  • arXiv
  • Open Source
  • multilingual

  Summary of Comments ( 72 )
  https://news.ycombinator.com/item?id=43690955

  Verbose tl;dr

  Hacker News users discussed the potential impact of the Teukens models, particularly their smaller size and focus on European languages, making them more accessible for researchers and individuals with limited resources. Several commenters expressed skepticism about the claimed performance, especially given the lack of public access and limited evaluation details. Others questioned the novelty, pointing out existing multilingual models and suggesting the main contribution might be the data collection process. The discussion also touched on the importance of open-sourcing models and the challenges of evaluating LLMs, particularly in non-English languages. Some users anticipated further analysis and comparisons once the models are publicly available.

  The Hacker News post titled "Teuken-7B-Base and Teuken-7B-Instruct: Towards European LLMs" (https://news.ycombinator.com/item?id=43690955) has a modest number of comments, sparking a discussion around several key themes related to the development and implications of European-based large language models (LLMs).

  Several commenters focused on the geopolitical implications of the project. One commenter expressed skepticism about the motivation behind creating "European" LLMs, questioning whether it stemmed from a genuine desire for technological sovereignty or simply a reaction to American dominance in the field. This spurred a discussion about the potential benefits of having diverse sources of LLM development, with some arguing that it could foster competition and innovation, while others expressed concern about fragmentation and duplication of effort. The idea of data sovereignty and the potential for different cultural biases in LLMs trained on European data were also touched upon.

  Another thread of discussion revolved around the technical aspects of the Teuken models. Commenters inquired about the specific hardware and training data used, expressing interest in comparing the performance of these models to existing LLMs. The licensing and accessibility of the models were also raised as points of interest. Some users expressed a desire for more transparency regarding the model's inner workings and training process.

  Finally, a few comments touched upon the broader societal implications of LLMs. One commenter questioned the usefulness of yet another LLM, suggesting that the focus should be on developing better applications and tools that utilize existing models, rather than simply creating more models. Another commenter raised the issue of potential misuse of LLMs and the importance of responsible development and deployment.

  While there wasn't a single overwhelmingly compelling comment, the discussion as a whole provides a valuable snapshot of the various perspectives surrounding the development of European LLMs, touching upon technical, geopolitical, and societal considerations. The comments highlight the complex interplay of factors that influence the trajectory of LLM development and the importance of open discussion and critical evaluation of these powerful technologies.

• Writing an LLM from scratch, part 8 – trainable self-attention

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  Posted: 2025-03-05 01:41:14

  Verbose tl;dr

  This blog post details the implementation of trainable self-attention, a crucial component of transformer-based language models, within the author's ongoing project to build an LLM from scratch. It focuses on replacing the previously hardcoded attention mechanism with a learned version, enabling the model to dynamically weigh the importance of different parts of the input sequence. The post covers the mathematical underpinnings of self-attention, including queries, keys, and values, and explains how these are represented and calculated within the code. It also discusses the practical implementation details, like matrix multiplication and softmax calculations, necessary for efficient computation. Finally, it showcases the performance improvements gained by using trainable self-attention, demonstrating its effectiveness in capturing contextual relationships within the text.

  This blog post, the eighth in a series on building a Large Language Model (LLM) from scratch, delves into the crucial concept of trainable self-attention, a mechanism that allows the model to weigh different parts of the input sequence differently when generating output. The author begins by recapping the previous implementation of self-attention, which relied on fixed, pre-computed attention weights based on the relative positions of tokens in the input sequence. This approach, while functional, lacked the flexibility and adaptability of a truly learned attention mechanism. He emphasizes that the core objective of this post is to enable the model to learn these attention weights during the training process, allowing the model to discover contextually relevant relationships between tokens that go beyond simple positional proximity.

  The transition to trainable self-attention involves introducing learnable parameters, specifically weight matrices, into the attention calculation. The author meticulously outlines the mathematical operations involved, starting with projecting the input embeddings into three distinct vector spaces: Query (Q), Key (K), and Value (V). These projections are accomplished through matrix multiplications with the corresponding weight matrices (W_Q, W_K, and W_V). The attention weights are then calculated by performing a dot product between the Query vector of each token and the Key vectors of all other tokens in the sequence. This dot product operation captures the affinity or relevance between different token pairs. These raw attention scores are then scaled down by the square root of the embedding dimension to prevent them from becoming too large and to stabilize training. A softmax function is then applied to these scaled scores, converting them into probabilities that sum to one for each token. Finally, these attention probabilities are used to compute a weighted average of the Value vectors, effectively allowing the model to attend to different parts of the input with varying degrees of focus.

  The author highlights the importance of backpropagation for training these newly introduced weight matrices. During backpropagation, the error signal from the output is propagated back through the network, and the gradients with respect to the attention weights are calculated. These gradients are then used to update the weight matrices via an optimization algorithm, typically stochastic gradient descent, thereby refining the attention mechanism over successive iterations of training.

  The post then provides a detailed walkthrough of the Python code implementation of this trainable self-attention mechanism, using the Jax framework for automatic differentiation and efficient computation. The code includes the necessary steps for initializing the weight matrices, performing the forward pass to calculate the attention-weighted output, and implementing the backward pass for gradient calculation and weight updates. The author stresses the clarity and conciseness of the Jax implementation, emphasizing its advantages for building and training complex models like LLMs. He concludes by reiterating the significance of this step in the development of a full-fledged LLM, paving the way for more sophisticated language understanding and generation capabilities.

  • Large Language Models
  • LLMs
  • natural language processing
  • NLP
  • artificial intelligence
  • AI
  • Self-Attention
  • attention mechanism
  • deep learning
  • machine learning
  • neural networks
  • training
  • from scratch
  • Implementation
  • Tutorial
  • coding
  • Software Development
  • Transformer Model
  • Language Model Training

  Summary of Comments ( 24 )
  https://news.ycombinator.com/item?id=43261650

  Verbose tl;dr

  Hacker News users discuss the blog post's approach to implementing self-attention, with several praising its clarity and educational value, particularly in explaining the complexities of matrix multiplication and optimization for performance. Some commenters delve into specific implementation details, like the use of torch.einsum and the choice of FlashAttention, offering alternative approaches and highlighting potential trade-offs. Others express interest in seeing the project evolve to handle longer sequences and more complex tasks. A few users also share related resources and discuss the broader landscape of LLM development. The overall sentiment is positive, appreciating the author's effort to demystify a core component of LLMs.

  The Hacker News post titled "Writing an LLM from scratch, part 8 – trainable self-attention" has generated several comments discussing various aspects of the linked blog post.

  Several commenters praise the author's clear and accessible explanation of complex concepts related to LLMs and self-attention. One commenter specifically appreciates the author's approach of starting with a simple, foundational model and gradually adding complexity, making it easier for readers to follow along. Another echoes this sentiment, highlighting the benefit of the step-by-step approach for understanding the underlying mechanics.

  There's a discussion around the practical implications of implementing such a model from scratch. A commenter questions the real-world usefulness of building an LLM from the ground up, given the availability of sophisticated pre-trained models and libraries. This sparks a counter-argument that emphasizes the educational value of such an endeavor, allowing for a deeper understanding of the inner workings of these models, even if it's not practically efficient for production use. The idea of building from scratch being a valuable learning experience, even if not practical for deployment, is a recurring theme.

  One commenter dives into a more technical discussion about the author's choice of softmax for the attention mechanism, suggesting alternative approaches like sparsemax. This leads to further conversation exploring the tradeoffs between different attention mechanisms in terms of performance and computational cost.

  Another thread focuses on the challenges of scaling these models. A commenter points out the computational demands of training large language models and how this limits accessibility for individuals or smaller organizations. This comment prompts a discussion on various optimization techniques and hardware considerations for efficient LLM training.

  Finally, some commenters express excitement about the ongoing series and look forward to future installments where the author will cover more advanced topics. The overall sentiment towards the blog post is positive, with many praising its educational value and clarity.