Awesome-Attention-Heads
An awesome repository & A comprehensive survey on interpretability of LLM attention heads.
Stars: 225
Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.
README:
[!IMPORTANT]
🌟 Star Us! If you find our work helpful, please consider staring our GitHub to stay updated with the latest content in this Awesome repo!
About this repo. This is a platform to get the latest research on different kinds of LLM's Attention Heads. Also, we released a survey based on these fantastic works.
If you want to cite our work, here is our bibtex entry: CITATION.bib.
If you only want to see the related paper list, please jump directly to here.
- [2024/09/07] Our paper secured the 2nd place on Hugging Face's Daily Paper List.
- [2024/09/06] Our survey paper is available on the arXiv platform: https://arxiv.org/abs/2409.03752.
With the development of Large Language Model (LLMs), their underlying network structure, the Transformer, is being extensively studied. Researching the Transformer structure helps us enhance our understanding of this "black box" and improve model interpretability. Recently, there has been an increasing body of work suggesting that the model contains two distinct partitions: attention mechanisms used for behavior, inference, and analysis, and Feed-Forward Networks (FFN) for knowledge storage. The former is crucial for revealing the functional capabilities of the model, leading to a series of studies exploring various functions within attention mechanisms, which we have termed Attention Head Mining.
In this survey, we delve into the potential mechanisms of how attention heads in LLMs contribute to the reasoning process.
Highlights:
- We propose an innovative four-stage framework, inspired by human cognitive neuroscience, to analyze the reasoning process of LLMs (Knowledge Recalling, In-Context Identification, Latent Reasoning, Expression Preparation).
- We classify current research on the interpretability of LLM attention heads according to the four-stage framework and d explore the collaborative mechanisms among them.
- We provide a comprehensive summary and classification of the experimental methodologies
- We summary the limitations of current research in this field and propose directions for future research.
Papers below are ordered by publication date:
Year 2024
| Date | Paper & Summary | Tags | Links |
| 2024-09-09 | Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers |  |
 |
|
• The paper introduces a generalized induction head mechanism, explaining how transformer components collaborate to perform in-context learning (ICL) on n-gram Markov chains. • It analyzes a two-attention-layer transformer with gradient flow to predict tokens in Markov chains. • Gradient flow converges, enabling ICL through a learned feature-based induction head mechanism. |
|||
| 2024-08-16 | A Mechanistic Interpretation of Syllogistic Reasoning in Auto-Regressive Language Models |  |
|
|
• The study introduces a mechanistic interpretation of syllogistic reasoning in LMs, identifying content-independent reasoning circuits. • Circuit discovery for reasoning and investigating belief bias contamination in attention heads. • Identified a necessary reasoning circuit transferable across syllogistic schemes, but susceptible to contamination by pre-trained world knowledge. |
|||
| 2024-08-01 | Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach |  |
 |
|
• Introduces a cost-effective model editing approach focusing on attention heads to enhance semantic consistency in LLMs without extensive parameter changes. • Analyzed attention heads, injected biases, and tested on NLU and NLG datasets. • Achieved notable improvements in semantic consistency and task performance, with strong generalization across additional tasks. |
|||
| 2024-07-31 | Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment |  |
|
|
• Introduced Negative Attention Score (NAS) to quantify and correct negative bias in language models. • Identified negatively biased attention heads and proposed Negative Attention Score Alignment (NASA) for fine-tuning. • NASA effectively reduced the precision-recall gap while preserving generalization in binary decision tasks. |
|||
| 2024-07-29 | Detecting and Understanding Vulnerabilities in Language Models via Mechanistic Interpretability |  |
|
|
• Introduces a method using Mechanistic Interpretability (MI) to detect and understand vulnerabilities in LLMs, particularly adversarial attacks. • Analyzes GPT-2 Small for vulnerabilities in predicting 3-letter acronyms. • Successfully identifies and explains specific vulnerabilities in the model related to the task. |
|||
| 2024-07-22 | RazorAttention: Efficient KV Cache Compression Through Retrieval Heads |  |
|
|
• Introduced RazorAttention, a training-free KV cache compression technique using retrieval heads and compensation tokens to preserve critical token information. • Evaluated RazorAttention on large language models (LLMs) for efficiency. • Achieved over 70% KV cache size reduction with no noticeable performance impact. |
|||
| 2024-07-21 | Answer, Assemble, Ace: Understanding How Transformers Answer Multiple Choice Questions |  |
|
|
• The paper introduces vocabulary projection and activation patching to localize hidden states that predict the correct MCQA answers. • Identified key attention heads and layers responsible for answer selection in transformers. • Middle-layer attention heads are crucial for accurate answer prediction, with a sparse set of heads playing unique roles. |
|||
| 2024-07-09 | Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning | |
|
|
• The article identifies induction heads as crucial for pattern matching in in-context learning (ICL). • Evaluated Llama-3-8B and InternLM2-20B on abstract pattern recognition and NLP tasks. • Ablating induction heads reduces ICL performance by up to ~32%, bringing it close to random for pattern recognition. |
|||
| 2024-07-01 | Steering Large Language Models for Cross-lingual Information Retrieval |
 
|
 |
|
• Introduces Activation Steered Multilingual Retrieval (ASMR), using steering activations to guide LLMs for improved cross-lingual information retrieval. • Identified attention heads in LLMs affecting accuracy and language coherence, and applied steering activations. • ASMR achieved state-of-the-art performance on CLIR benchmarks like XOR-TyDi QA and MKQA. |
|||
| 2024-06-21 | MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression |  |
|
|
• The paper introduces Mixture of Attention (MoA), which tailors distinct sparse attention configurations for different heads and layers, optimizing memory, throughput, and accuracy-latency trade-offs. • MoA profiles models, explores attention configurations, and improves LLM compression. • MoA increases effective context length by 3.9×, while reducing GPU memory usage by 1.2-1.4×. |
|||
| 2024-06-19 | On the Difficulty of Faithful Chain-of-Thought Reasoning in Large Language Models |  |
|
|
• Introduced novel strategies for in-context learning, fine-tuning, and activation editing to improve Chain-of-Thought (CoT) reasoning faithfulness in LLMs. • Tested these strategies across multiple benchmarks to evaluate their effectiveness. • Found only limited success in enhancing CoT faithfulness, highlighting the challenge in achieving truly faithful reasoning in LLMs. |
|||
| 2024-06-04 | Iteration Head: A Mechanistic Study of Chain-of-Thought |  |
|
|
• Introduces "iteration heads," specialized attention heads that enable iterative reasoning in transformers for Chain-of-Thought (CoT) tasks. • Analysis of attention mechanisms, tracking CoT emergence, and testing CoT skills' transferability between tasks. • Iteration heads effectively support CoT reasoning, improving model interpretability and task performance. |
|||
| 2024-06-03 | LoFiT: Localized Fine-tuning on LLM Representations | |
|
|
• Introduces Localized Fine-tuning on LLM Representations (LoFiT), a two-step framework to identify important attention heads of a given task and learn task-specific offset vectors to intervene on the representations of the identified heads. • Identified sparse sets of important attention heads for improving downstream accuracy on truthfulness and reasoning. • LoFiT outperformed other representation intervention methods and achieved comparable performance to PEFT methods on TruthfulQA, CLUTRR, and MQuAKE, despite only intervening on 10% of the total attention heads in LLMs. |
|||
| 2024-05-28 | Knowledge Circuits in Pretrained Transformers |
 
|
|
|
• Introduced "knowledge circuits" in transformers, revealing how specific knowledge is encoded through interaction among attention heads, relation heads, and MLPs. • Analyzed GPT-2 and TinyLLAMA to identify knowledge circuits; evaluated knowledge editing techniques. • Demonstrated how knowledge circuits contribute to model behaviors like hallucinations and in-context learning. |
|||
| 2024-05-23 | Linking In-context Learning in Transformers to Human Episodic Memory | |
|
|
• Links in-context learning in Transformer models to human episodic memory, highlighting similarities between induction heads and the contextual maintenance and retrieval (CMR) model. • Analysis of Transformer-based LLMs to demonstrate CMR-like behavior in attention heads. • CMR-like heads emerge in intermediate layers, mirroring human memory biases. |
|||
| 2024-05-07 | How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit via Mechanistic Interpretability |  |
|
|
• First mechanistic interpretability study on GPT-2 for predicting multi-token acronyms using attention heads. • Identified and interpreted a circuit of 8 attention heads responsible for acronym prediction. • Demonstrated that these 8 heads (~5% of total) concentrate the acronym prediction functionality. |
|||
| 2024-05-02 | What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation | |
|
|
• Introduced an optogenetics-inspired causal framework to study induction head (IH) formation in transformers. • Analyzed IH emergence in transformers using synthetic data and identified three underlying subcircuits responsible for IH formation. • Discovered that these subcircuits interact to drive IH formation, coinciding with a phase change in model loss. |
|||
| 2024-04-24 | Retrieval Head Mechanistically Explains Long-Context Factuality | |
|
|
• Identified "retrieval heads" in transformer models responsible for retrieving information across long contexts. • Systematic investigation of retrieval heads across various models, including analysis of their role in chain-of-thought reasoning. • Pruning retrieval heads leads to hallucination, while pruning non-retrieval heads doesn't affect retrieval ability. |
|||
| 2024-03-27 | Non-Linear Inference Time Intervention: Improving LLM Truthfulness |  |
|
|
• Introduced Non-Linear Inference Time Intervention (NL-ITI), enhancing LLM truthfulness by multi-token probing and intervention without fine-tuning. • Evaluated NL-ITI on multiple-choice datasets, including TruthfulQA. • Achieved a 16% relative improvement in MC1 accuracy on TruthfulQA over baseline ITI. |
|||
| 2024-02-28 | Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models |
|
|
|
• Introduces the PH3 method to prune conflicting attention heads, mitigating knowledge conflicts in language models without parameter updates. • Applied PH3 to control LMs' reliance on internal memory vs. external context and tested its effectiveness on open-domain QA tasks. • PH3 improved internal memory usage by 44.0% and external context usage by 38.5%. |
|||
| 2024-02-27 | Information Flow Routes: Automatically Interpreting Language Models at Scale |
|
|
|
• Introduces "Information Flow Routes" using attribution for graph-based interpretation of language models, avoiding activation patching. • Experiments with Llama 2, identifying key attention heads and behavior patterns across different domains and tasks. • Uncovered specialized model components; identified consistent roles for attention heads, such as handling tokens of the same part of speech. |
|||
| 2024-02-20 | Identifying Semantic Induction Heads to Understand In-Context Learning | |
|
|
• Identifies and studies "semantic induction heads" in large language models (LLMs) that correlate with in-context learning abilities. • Analyzed attention heads for encoding syntactic dependencies and knowledge graph relations. • Certain attention heads enhance output logits by recalling relevant tokens, crucial for understanding in-context learning in LLMs. |
|||
| 2024-02-16 | The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains | |
|
|
• Introduces a Markov Chain sequence modeling task to analyze how in-context learning (ICL) capabilities emerge in transformers, forming "statistical induction heads." • Empirical and theoretical investigation of multi-phase training in transformers on Markov Chain tasks. • Demonstrates phase transitions from unigram to bigram predictions, influenced by transformer layer interactions. |
|||
| 2024-02-11 | Summing Up the Facts: Additive Mechanisms Behind Factual Recall in LLMs |
|
|
|
• Identifies and explains the "additive motif" in factual recall, where LLMs use multiple independent mechanisms that constructively interfere to recall facts. • Extended direct logit attribution to analyze attention heads and unpacked the behavior of mixed heads. • Demonstrated that factual recall in LLMs results from the sum of multiple, independently insufficient contributions. |
|||
| 2024-02-05 | How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning |  |
|
|
• Introduces the concept that query and key matrices in in-context heads operate as "two towers" for metric learning, facilitating similarity computation between label features. • Analyzed in-context learning mechanisms; identified specific attention heads crucial for ICL. • Reduced ICL accuracy from 87.6% to 24.4% by intervening in only 1% of these heads. |
|||
| 2024-01-23 | In-Context Language Learning: Architectures and Algorithms | |
|
|
• Introduction of "n-gram heads," specialized Transformer attention heads, enhancing in-context language learning (ICLL) through input-conditional token prediction. • Evaluated neural models on regular languages from random finite automata. • Hard-wiring n-gram heads improved perplexity by 6.7% on the SlimPajama dataset. |
|||
| 2024-01-16 | The mechanistic basis of data dependence and abrupt learning in an in-context classification task | |
|
|
• The paper models the mechanistic basis of in-context learning (ICL) via the abrupt formation of induction heads in attention-only networks. • Simulated ICL tasks using simplified input data and a two-layer attention-based network. • Induction head formation drives the abrupt transition to ICL, traced through nested non-linearities. |
|||
| 2024-01-16 | Circuit Component Reuse Across Tasks in Transformer Language Models |  |
|
|
• The paper demonstrates that specific circuits in GPT-2 can generalize across different tasks, challenging the notion that such circuits are task-specific. • It examines the reuse of circuits from the Indirect Object Identification (IOI) task in the Colored Objects task. • Adjusting four attention heads boosts accuracy from 49.6% to 93.7% in the Colored Objects task. |
|||
| 2024-01-16 | Successor Heads: Recurring, Interpretable Attention Heads In The Wild |  |
 |
|
• The paper introduces "Successor Heads," attention heads in LLMs that increment tokens with natural orderings, like days or numbers. • It analyzes the formation of successor heads across various model sizes and architectures, such as GPT-2 and Llama-2. • Successor heads are found in models ranging from 31M to 12B parameters, revealing abstract, recurring numeric representations. |
|||
| 2024-01-16 | Function Vectors in Large Language Models |  |
|
|
• The article introduces "Function Vectors (FVs)," compact, causal representations of tasks within autoregressive transformer models. • FVs were tested across diverse in-context learning (ICL) tasks, models, and layers. • FVs can be summed to create vectors that trigger new, complex tasks, demonstrating internal vector composition. |
|||
Year 2023
| Date | Paper & Summary | Tags | Links |
| 2023-12-23 | Fact Finding: Attempting to Reverse-Engineer Factual Recall on the Neuron Level |  |
 |
|
• Investigated how early MLP layers in Pythia 2.8B encode factual recall using distributed circuits, focusing on superposition and multi-token embeddings. • Explored factual lookup in MLP layers, tested hypotheses on detokenization and hashing mechanisms. • Factual recall functions like a distributed look-up table without easily interpretable internal mechanisms. |
|||
| 2023-10-23 | Linear Representations of Sentiment in Large Language Models |  |
|
|
• The paper identifies a linear direction in activation space that captures sentiment representation in Large Language Models (LLMs). • They isolated this sentiment direction and tested it on tasks including Stanford Sentiment Treebank. • Ablating this sentiment direction leads to a 76% reduction in classification accuracy, highlighting its importance. |
|||
| 2023-10-06 | Copy Suppression: Comprehensively Understanding an Attention Head |  |
|
|
• The paper introduces the concept of copy suppression in a GPT-2 Small attention head (L10H7), which reduces naive token copying, enhancing model calibration. • The paper investigates and explains the mechanism of copy suppression and its role in self-repair. • 76.9% of L10H7's impact in GPT-2 Small is explained, making it the most comprehensive description of an attention head's role. |
|||
| 2023-09-22 | Inference-Time Intervention: Eliciting Truthful Answers from a Language Model | |
|
|
• Introduced Inference-Time Intervention (ITI) to enhance LLM truthfulness by adjusting model activations in select attention heads. • Improved LLaMA model performance on the TruthfulQA benchmark. • ITI increased Alpaca model's truthfulness from 32.5% to 65.1%. |
|||
| 2023-09-22 | Birth of a Transformer: A Memory Viewpoint | |
|
|
• The paper presents a memory-based perspective on transformers, highlighting associative memories in weight matrices and their gradient-driven learning. • Empirical analysis of training dynamics on a simplified transformer model with synthetic data. • Discovery of rapid global bigram learning and the slower emergence of an "induction head" for in-context bigrams. |
|||
| 2023-09-13 | Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs |  |
|
|
• Identifies Syntactic Attention Structure (SAS) as a naturally emerging property in masked language models (MLMs) and its role in syntax acquisition. • Analyzes SAS during training and manipulates it to study its causal effect on grammatical capabilities. • SAS is necessary for grammar development, but briefly suppressing it improves model performance. |
|||
| 2023-07-18 | Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla |
   
|
|
|
• Scalable circuit analysis applied to a 70B Chinchilla language model for understanding multiple-choice question answering. • Logit attribution, attention pattern visualization, and activation patching to identify and categorize key attention heads. • Identified "Nth item in an enumeration" feature in attention heads, though it's only a partial explanation. |
|||
| 2023-02-02 | Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small |
     
|
|
|
• The paper introduces a detailed explanation of how GPT-2 small performs indirect object identification (IOI) using a large circuit involving 28 attention heads grouped into 7 classes. • They reverse-engineered the IOI task in GPT-2 small using causal interventions and projections. • The study demonstrates that mechanistic interpretability of large language models is feasible. |
|||
Before ChatGPT Announced
| Date | Paper & Summary | Tags | Links |
| 2022-03-08 | In-context Learning and Induction Heads | |
 |
|
• The paper identifies "induction heads" in Transformer models, which enable in-context learning by recognizing and copying patterns in sequences. • Analyzes attention patterns and induction heads across various layers in different Transformer models. • Found that induction heads are crucial for enabling Transformers to generalize and perform in-context learning tasks effectively. |
|||
| 2021-12-22 | A Mathematical Framework for Transformer Circuits | |
|
|
• Introduces a mathematical framework to reverse-engineer small attention-only transformers, focusing on understanding attention heads as independent, additive components. • Analyzed zero, one, and two-layer transformers to identify the role of attention heads in information movement and composition. • Discovered "induction heads," crucial for in-context learning in two-layer transformers. |
|||
| 2021-05-18 | The Heads Hypothesis: A Unifying Statistical Approach Towards Understanding Multi-Headed Attention in BERT |
|
|
|
• The paper proposes a novel method called "Sparse Attention" that reduces the computational complexity of attention mechanisms by selectively focusing on important tokens. • The method was evaluated on machine translation and text classification tasks. • The sparse attention model achieves comparable accuracy to dense attention while significantly reducing computational cost. |
|||
| 2021-04-01 | Have Attention Heads in BERT Learned Constituency Grammar? |  |
 |
|
• The study introduces a syntactic distance method to analyze constituency grammar in BERT and RoBERTa attention heads. • Constituency grammar was extracted and analyzed pre- and post-fine-tuning on SMS and NLI tasks. • NLI tasks increase constituency grammar inducing ability, while SMS tasks decrease it in upper layers. |
|||
| 2019-11-27 | Do Attention Heads in BERT Track Syntactic Dependencies? |  |
|
|
• The paper investigates whether individual attention heads in BERT capture syntactic dependencies, using attention weights to extract dependency relations. • Analyzed BERT's attention heads using maximum attention weights and maximum spanning trees, comparing them to Universal Dependency trees. • Some attention heads track specific syntactic dependencies better than baselines, but no head performs holistic parsing significantly better. |
|||
| 2019-11-01 | Adaptively Sparse Transformers |
|
|
|
• Introduced the adaptively sparse Transformer using alpha-entmax to allow flexible, context-dependent sparsity in attention heads. • Applied to machine translation datasets to assess interpretability and head diversity. • Achieved diverse attention distributions and improved interpretability without compromising accuracy. |
|||
| 2019-08-01 | What does BERT look at? An Analysis of BERT’s Attention | |
|
|
• The paper introduces methods to analyze BERT's attention mechanisms, revealing patterns that align with linguistic structures like syntax and coreference. • Analysis of attention heads, identification of syntactic and coreferential patterns, and development of an attention-based probing classifier. • BERT's attention heads capture substantial syntactic information, particularly in tasks like identifying direct objects and coreference. |
|||
| 2019-07-01 | Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned |
|
|
|
• The paper introduces a novel pruning method for multi-head self-attention that selectively removes less important heads without major performance loss. • Analysis of individual attention heads, identification of their specialized roles, and application of a pruning method on the Transformer model. • Pruning 38 out of 48 heads in the encoder led to only a 0.15 BLEU score drop. |
|||
| 2018-11-01 | An Analysis of Encoder Representations in Transformer-Based Machine Translation | |
|
|
• This paper analyzes the internal representations of Transformer encoder layers, focusing on syntactic and semantic information learned by self-attention heads. • Probing tasks, dependency relation extraction, and a transfer learning scenario. • Lower layers capture syntax, while higher layers encode more semantic information. |
|||
| 2016-03-21 | Incorporating Copying Mechanism in Sequence-to-Sequence Learning | |
|
|
• Introduces a copying mechanism into sequence-to-sequence models to allow direct copying of input tokens, improving handling of rare words. • Applied to machine translation and summarization tasks. • Achieved substantial improvements in translation accuracy, especially on rare word translation, compared to standard sequence-to-sequence models. |
|||
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for Awesome-Attention-Heads
Similar Open Source Tools
Awesome-Attention-Heads
Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.
MiniCPM-V
MiniCPM-V is a series of end-side multimodal LLMs designed for vision-language understanding. The models take image and text inputs to provide high-quality text outputs. The series includes models like MiniCPM-Llama3-V 2.5 with 8B parameters surpassing proprietary models, and MiniCPM-V 2.0, a lighter model with 2B parameters. The models support over 30 languages, efficient deployment on end-side devices, and have strong OCR capabilities. They achieve state-of-the-art performance on various benchmarks and prevent hallucinations in text generation. The models can process high-resolution images efficiently and support multilingual capabilities.
openrl
OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.
PIXIU
PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.
DB-GPT
DB-GPT is an open source AI native data app development framework with AWEL(Agentic Workflow Expression Language) and agents. It aims to build infrastructure in the field of large models, through the development of multiple technical capabilities such as multi-model management (SMMF), Text2SQL effect optimization, RAG framework and optimization, Multi-Agents framework collaboration, AWEL (agent workflow orchestration), etc. Which makes large model applications with data simpler and more convenient.
txtai
Txtai is an all-in-one embeddings database for semantic search, LLM orchestration, and language model workflows. It combines vector indexes, graph networks, and relational databases to enable vector search with SQL, topic modeling, retrieval augmented generation, and more. Txtai can stand alone or serve as a knowledge source for large language models (LLMs). Key features include vector search with SQL, object storage, topic modeling, graph analysis, multimodal indexing, embedding creation for various data types, pipelines powered by language models, workflows to connect pipelines, and support for Python, JavaScript, Java, Rust, and Go. Txtai is open-source under the Apache 2.0 license.
rageval
Rageval is an evaluation tool for Retrieval-augmented Generation (RAG) methods. It helps evaluate RAG systems by performing tasks such as query rewriting, document ranking, information compression, evidence verification, answer generation, and result validation. The tool provides metrics for answer correctness and answer groundedness, along with benchmark results for ASQA and ALCE datasets. Users can install and use Rageval to assess the performance of RAG models in question-answering tasks.
LocalAI
LocalAI is a free and open-source OpenAI alternative that acts as a drop-in replacement REST API compatible with OpenAI (Elevenlabs, Anthropic, etc.) API specifications for local AI inferencing. It allows users to run LLMs, generate images, audio, and more locally or on-premises with consumer-grade hardware, supporting multiple model families and not requiring a GPU. LocalAI offers features such as text generation with GPTs, text-to-audio, audio-to-text transcription, image generation with stable diffusion, OpenAI functions, embeddings generation for vector databases, constrained grammars, downloading models directly from Huggingface, and a Vision API. It provides a detailed step-by-step introduction in its Getting Started guide and supports community integrations such as custom containers, WebUIs, model galleries, and various bots for Discord, Slack, and Telegram. LocalAI also offers resources like an LLM fine-tuning guide, instructions for local building and Kubernetes installation, projects integrating LocalAI, and a how-tos section curated by the community. It encourages users to cite the repository when utilizing it in downstream projects and acknowledges the contributions of various software from the community.
Awesome-AI-Data-GitHub-Repos
Awesome AI & Data GitHub-Repos is a curated list of essential GitHub repositories covering the AI & ML landscape. It includes resources for Natural Language Processing, Large Language Models, Computer Vision, Data Science, Machine Learning, MLOps, Data Engineering, SQL & Database, and Statistics. The repository aims to provide a comprehensive collection of projects and resources for individuals studying or working in the field of AI and data science.
LLM4Decompile
LLM4Decompile is an open-source large language model dedicated to decompilation of Linux x86_64 binaries, supporting GCC's O0 to O3 optimization levels. It focuses on assessing re-executability of decompiled code through HumanEval-Decompile benchmark. The tool includes models with sizes ranging from 1.3 billion to 33 billion parameters, available on Hugging Face. Users can preprocess C code into binary and assembly instructions, then decompile assembly instructions into C using LLM4Decompile. Ongoing efforts aim to expand capabilities to support more architectures and configurations, integrate with decompilation tools like Ghidra and Rizin, and enhance performance with larger training datasets.
Open-Interface
Open Interface is a self-driving software that automates computer tasks by sending user requests to a language model backend (e.g., GPT-4V) and simulating keyboard and mouse inputs to execute the steps. It course-corrects by sending current screenshots to the language models. The tool supports MacOS, Linux, and Windows, and requires setting up the OpenAI API key for access to GPT-4V. It can automate tasks like creating meal plans, setting up custom language model backends, and more. Open Interface is currently not efficient in accurate spatial reasoning, tracking itself in tabular contexts, and navigating complex GUI-rich applications. Future improvements aim to enhance the tool's capabilities with better models trained on video walkthroughs. The tool is cost-effective, with user requests priced between $0.05 - $0.20, and offers features like interrupting the app and primary display visibility in multi-monitor setups.
RLHF-Reward-Modeling
This repository, RLHF-Reward-Modeling, is dedicated to training reward models for DRL-based RLHF (PPO), Iterative SFT, and iterative DPO. It provides state-of-the-art performance in reward models with a base model size of up to 13B. The installation instructions involve setting up the environment and aligning the handbook. Dataset preparation requires preprocessing conversations into a standard format. The code can be run with Gemma-2b-it, and evaluation results can be obtained using provided datasets. The to-do list includes various reward models like Bradley-Terry, preference model, regression-based reward model, and multi-objective reward model. The repository is part of iterative rejection sampling fine-tuning and iterative DPO.
InternLM-XComposer
InternLM-XComposer2 is a groundbreaking vision-language large model (VLLM) based on InternLM2-7B excelling in free-form text-image composition and comprehension. It boasts several amazing capabilities and applications: * **Free-form Interleaved Text-Image Composition** : InternLM-XComposer2 can effortlessly generate coherent and contextual articles with interleaved images following diverse inputs like outlines, detailed text requirements and reference images, enabling highly customizable content creation. * **Accurate Vision-language Problem-solving** : InternLM-XComposer2 accurately handles diverse and challenging vision-language Q&A tasks based on free-form instructions, excelling in recognition, perception, detailed captioning, visual reasoning, and more. * **Awesome performance** : InternLM-XComposer2 based on InternLM2-7B not only significantly outperforms existing open-source multimodal models in 13 benchmarks but also **matches or even surpasses GPT-4V and Gemini Pro in 6 benchmarks** We release InternLM-XComposer2 series in three versions: * **InternLM-XComposer2-4KHD-7B** 🤗: The high-resolution multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _High-resolution understanding_ , _VL benchmarks_ and _AI assistant_. * **InternLM-XComposer2-VL-7B** 🤗 : The multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _VL benchmarks_ and _AI assistant_. **It ranks as the most powerful vision-language model based on 7B-parameter level LLMs, leading across 13 benchmarks.** * **InternLM-XComposer2-VL-1.8B** 🤗 : A lightweight version of InternLM-XComposer2-VL based on InternLM-1.8B. * **InternLM-XComposer2-7B** 🤗: The further instruction tuned VLLM for _Interleaved Text-Image Composition_ with free-form inputs. Please refer to Technical Report and 4KHD Technical Reportfor more details.
sailor-llm
Sailor is a suite of open language models tailored for South-East Asia (SEA), focusing on languages such as Indonesian, Thai, Vietnamese, Malay, and Lao. Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of the SEA region. Built from Qwen 1.5, Sailor encompasses models of varying sizes, spanning from 0.5B to 7B versions for different requirements. Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, reading comprehension, and more in SEA languages.
timefold-solver
Timefold Solver is an optimization engine evolved from OptaPlanner. Developed by the original OptaPlanner team, our aim is to free the world of wasteful planning.
For similar tasks
Awesome-Attention-Heads
Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.
For similar jobs
weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.
LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.
kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.
tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.
spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.
Magick
Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.