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o scale our data processing capabilities, but optimize costs. Download the Zyda-2 dataset directly from Hugging Face and train higher-accuracy models. For more information see our GitHub tutorial. Appendix Analysis of Global Duplicates Figure 7: Distribution of cluster sizes of duplicates in global dataset (log-log scale). Figure 8: Distribution of cluster sizes of duplicates in DCLM (log-log scale). Figure 9: Distribution of cluster sizes of duplicates in FineWeb-Edu2 (log-log scale). Figure 10

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ode you want on that API, but if they have weights access they’re by default going to be able to do whatever they want; Internal tools, e.g. Slack, wikis, internal arxiv, internal github. Like the internet, you could either access these through the scaffold or from the execution server Different ways the AI could compromise this Model weight exfiltration The simplest way to imagine something going wrong is that you gave the agent access to sensitive model weights on the execution server, and it

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bove figure. As a novel tool in PaperQA2, we added a citation traversal tool, which we’ll dive into in a later section. PaperQA2 agentic configuration options can be found here in Github. We’ve included some descriptive statistics of PaperQA2’s agentic actions averaged across 3 LitQA2 benchmark runs (table below). Here we see that the agent is calling more than 4 tool calls per question on average, indicating that it’s not simply calling the tools in a deterministic order. This is especially app

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LLM. This can be done as a post-processing step to detect hallucinations: For an example of how to use Bespoke-Minicheck in a RAG application using Ollama, see the RAG example on GitHub. Getting started Start by downloading and running the model: Next, write the prompt as follows, providing both the source document and the claim: Since the source information supports the claim, the model will output Yes. However, when the claim is not supported by the document, the model will respond with No: F

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nted research direction like code generation. Existing coding benchmarks mostly measure performances in a one-pass generation setting; however, for AI to automate solving ordinary Github issues (like in SWE-bench ), it’s unlikely to be achieved by a single action—the AI needs to communicate back and forth with human software engineers to make sure it understands the correct requirements, ask for missing documentation and data, and even ask humans to give it a hand if needed. In a similar vein to

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as well as captured detailed metrics for time-to-first-token (TTFT) and time-per-output-token (TPOT), please refer to the appendix for more data and analysis. You can follow this github issue to reproduce our benchmark. Limitation of current optimizations. Although our current optimizations give a significant throughput gain, there are performance trade-offs from our current optimizations, especially from multi-step scheduling: Bumpy inter-token latency: In our current implementation of multi-s

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into building good ones. The purpose of this cookbook is to enable other technical groups to hit the ground running when building their own hybrid (SSM, Transformer, MoE) models. GitHub For context, we at Zyphra have built the following hybrid models: BlackMamba; Zamba-7B; Zamba2-2.7B; Zamba2-1.2B The following datasets: Zyda And the following engineering optimizations Tree Attention Dense transformer models (i.e. alternating multi-head attention (MHA) and multilayer perceptron (MLP) blocks) ha

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e of the richest coding data in the industry. Replit data is unique. We store over 300 million software repositories, in the same ballpark of the world’s largest coding hosts like GitHub and Bitbucket. We have a deeply granular understanding of the timeline of each project, thanks to a protocol called Operational Transformation (OT), and to the execution data logged when developers run their programs. The timeline is also enriched with error stack traces and LSP diagnostic logs, so we have debug

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d the notion of a “human baseline” for harder tasks may cease to be meaningful. ‍ Future Outlook: We have released 80% of the LAB-Bench dataset today (available at HuggingFace and GitHub .) We are withholding a private test set of 20% that we will use to monitor for training contamination (models being overfit to the benchmark). Moreover, the process that we have developed internally for building these evals is scalable and extensible, and we expect to leverage it to create more challenging benc

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e is manually labeled with fine-grained annotations on hallucination error types. Cited as: Weng, Lilian. (Jul 2024). Extrinsic Hallucinations in LLMs. Lil’Log. https://lilianweng.github.io/posts/2024-07-07-hallucination/. Or [1] Ji et al. “Survey of hallucination in natural language generation.” ACM Computing Surveys (2022) [2] Gekhman et al. “Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?” arXiv preprint arXiv:2405.05904 (2024). [3] Min et al. “FActScore: Fine-grained atomic

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documentation site. Assuming you have a chat model set up already (e.g. Codestral, Llama 3), you can keep this entire experience local by providing a link to the Ollama README on GitHub and asking questions to learn more with it as context. a. Type @docs in the chat sidebar, select “Add Docs”, copy and paste “https://github.com/ollama/ollama” into the URL field, and type “Ollama” into the title field b. It should quickly index the Ollama README and then you can ask it questions with important s

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gh it is primarily a research codebase not intended for production use, its contributions to the field are significant. You can explore and contribute to the project here: https://github.com/yandex-research/swarm. SWARM is built upon the hivemind project: Hivemind Hivemind is a framework to perform various distributed training algorithms. It has been used to train a text to image model across a pool of volunteer compute. At its core, Hivemind utilizes a distributed hash table spread across each

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orresponding short clip is synthesized sequentially conditioned on the keyframes Cited as: Weng, Lilian. (Apr 2024). Diffusion Models Video Generation. Lil’Log. https://lilianweng.github.io/posts/2024-04-12-diffusion-video/. Or [1] Cicek et al. 2016. “3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation.” [2] Ho & Salimans, et al. “Video Diffusion Models.” 2022 | webpage [3] Bar-Tal et al. 2024 “Lumiere: A Space-Time Diffusion Model for Video Generation.” [4] Brooks et al. “Vi

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understand what it's like to google "learn LLM"; then see 3 sponsored posts asking you to download e-books (in which nothing concrete appears)... then see 20 ultimate roadmaps and GitHub repos, where step 1/54 is to view a 2-hour long video (and no one knows what step 54 is because it's so looooooooong). So, instead of putting you through this pain myself, let's just break down what LLMs are in 3 key ideas: In ChatGPT, you input a piece of text, and it returns text, right? Well, what's actually

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s a popular open-source framework by Microsoft for building multi-agent applications. For this, example we’ll use the Code Llama model: Install Autogen: Then create a Python script example.py to use Ollama with Autogen: Lastly, run the example to have the assistant write the code to plot a chart: More to come This is initial experimental support for the OpenAI API. Future improvements under consideration include: GitHub issues are welcome! For more information, see the OpenAI compatibility docs.

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e trusted candidate set $\mathcal{C}$ and more noisy samples are removed. Cited as: Weng, Lilian. (Feb 2024). “Thinking about High-Quality Human Data”. Lil’Log. https://lilianweng.github.io/posts/2024-02-05-human-data-quality/. Or [1] Francis Galton “Vox populi” Nature 75, 450-451 (1907). [2] Sambasivan et al. “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI" CHI 2021 [3] Chris Callison-Burch. “Fast, Cheap, and Creative: Evaluating Translation Quality Usi

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team at Allen Institute for AI , whose API for exploring the citation graph of science is a key feature of PaperQA. We plan to make the full WikiCrow and PaperQA code available on GitHub soon. Until then, the essential aspects of the PaperQA algorithm are available (although you will need access to your own repository of full text scientific articles), as well as the prompts used for WikiCrow . Benchmarking PaperQA In our evaluations, PaperQA outperforms GPT-4, Perplexity, and other LLMs, as wel

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ctiveness is measured by attack objective functions designed for different experiments: - In text-to-image experiment, they used Q16 (Schramowski et al. 2022) and NudeNet (https://github.com/notAI-tech/NudeNet). - text-to-text experiment: TOXIGEN; Diversity is measured by pairwise dissimilarity, in form of $\sum {(\mathbf{x} i, \mathbf{x} j) \in \text{All pairs}} [1 - \text{sim}(\mathbf{x} i, \mathbf{x} j)]$; Low-toxicity is measured by Perspective API; Scoring-LIFO: Combine LIFO and Scoring str

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任务高度相关的目标算法，并且处理器是使用基于这些算法构建的定制执行数据集来训练的。这通常既需要领域专业知识，也需要计算机科学专业知识，因此构成了明显的入门门槛！ 自从我开始从事NAR以来，我一直对降低这一入门门槛抱有浓厚兴趣，同时也希望提供一组在各种任务中都应该有用的“基础处理器”。这促成了为期两年的工程努力，最终开源了CLRS算法推理基准（https://github.com/deepmind/clrs）[44]。 CLRS基准的灵感来自Cormen、Leiserson、Rivest和Stein的经典教材《算法导论》（CLRS）[1]，这是关于经典算法和数据结构最受欢迎的本科教材之一，也是竞赛程序员的“圣经”。尽管它有数千页之多，却只包含约90种不同的算法，而这些算法往往构成了整个软件工程职业生涯的基础！因此，CLRS中的算法可以构成我们所需的坚实的“基础处理器”集合，我们着手让构建和训练NAR处理器来执行CLRS中的算法变得容易。 在其第一个版本中，我们发布了CLRS-30，这是CLRS中三十种算法的数据集和处理器生成器集合，涵盖多种技能：排序、搜索、动态规划、图算法、字符串算法和

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mechanisms; Try it: ollama run nous-hermes-llama2; Eric Hartford’s Wizard Vicuna 13B uncensored Llama 1 13B model fine-tuned to remove alignment; Try it: ollama run wizard-vicuna Example output comparisons We’ve tried running the 7B Llama 2 model against the 7B llama2 uncensored model with the same prompts. Below are the outputs. Disclaimer: Uncensored models carry their own risk. Please use them accordingly. Try running uncensored models yourself with Ollama: https://github.com/jmorganca/ollama

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