# Falcon Mamba: The First Competitive Attention-free 7B Language Model

arXiv: https://arxiv.org/abs/2410.05355 · HTML: https://mlsanity.com/paper/2410.05355/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Placed in Core SSM architectures for language, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 40 of its 43 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Core SSM architectures for language](https://mlsanity.com/ssm/core-ssm-architectures-for-language/)

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Instruction-Following Evaluation for Large Language Models](https://mlsanity.com/paper/2311.07911/) (2023)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [RWKV: Reinventing RNNs for the Transformer Era](https://mlsanity.com/paper/2305.13048/) (2023)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies](https://mlsanity.com/paper/2404.06395/) (2024)
- [Scaling Vision Transformers to 22 Billion Parameters](https://mlsanity.com/paper/2302.05442/) (2023)
- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [Jamba: A Hybrid Transformer-Mamba Language Model](https://mlsanity.com/paper/2403.19887/) (2024)
- [Llemma: An Open Language Model For Mathematics](https://mlsanity.com/paper/2310.10631/) (2023)
- [The Stack: 3 TB of permissively licensed source code](https://mlsanity.com/paper/2211.15533/) (2022)
- [Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models](https://mlsanity.com/paper/2402.19427/) (2024)
- [MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning](https://mlsanity.com/paper/2310.16049/) (2023)
- [An Empirical Study of Mamba-based Language Models](https://mlsanity.com/paper/2406.07887/) (2024)

## Built on by (most cited first, 30 of 46)

- [STORM: Token-Efficient Long Video Understanding for Multimodal LLMs](https://mlsanity.com/paper/2503.04130/) (2025)
- [LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement](https://mlsanity.com/paper/2504.16053/) (2025)
- [Vamba: Understanding Hour-Long Videos with Hybrid Mamba-Transformers](https://mlsanity.com/paper/2503.11579/) (2025)
- [Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing](https://mlsanity.com/paper/2502.14458/) (2025)
- [Efficient Attention Mechanisms for Large Language Models: A Survey](https://mlsanity.com/paper/2507.19595/) (2025)
- [Olmo Hybrid: From Theory to Practice and Back](https://mlsanity.com/paper/2604.03444/) (2026)
- [The Zamba2 Suite: Technical Report](https://mlsanity.com/paper/2411.15242/) (2024)
- [Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure](https://mlsanity.com/paper/2412.15176/) (2024)
- [xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference](https://mlsanity.com/paper/2503.13427/) (2025)
- [LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing](https://mlsanity.com/paper/2507.00029/) (2025)
- [Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism](https://mlsanity.com/paper/2504.18574/) (2025)
- [ParaRNN: Unlocking Parallel Training of Nonlinear RNNs for Large Language Models](https://mlsanity.com/paper/2510.21450/) (2025)
- [LoLA: Low-Rank Linear Attention With Sparse Caching](https://mlsanity.com/paper/2505.23666/) (2025)
- [Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasis](https://mlsanity.com/paper/2411.19655/) (2024)
- [Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models](https://mlsanity.com/paper/2501.17088/) (2025)
- [Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation](https://mlsanity.com/paper/2510.02279/) (2025)
- [TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba](https://mlsanity.com/paper/2502.15130/) (2025)
- [Overflow Prevention Enhances Long-Context Recurrent LLMs](https://mlsanity.com/paper/2505.07793/) (2025)
- [Convergent Evolution: How Different Language Models Learn Similar Number Representations](https://mlsanity.com/paper/2604.20817/) (2026)
- [SparseSSM: Efficient Selective Structured State Space Models Can Be Pruned in One-Shot](https://mlsanity.com/paper/2506.09613/) (2025)
- [SpikingBrain: Spiking Brain-inspired Large Models](https://mlsanity.com/paper/2509.05276/) (2025)
- [Understanding and Enhancing Mamba-Transformer Hybrids for Memory Recall and Language Modeling](https://mlsanity.com/paper/2510.26912/) (2025)
- [On the Expressivity of Selective State-Space Layers: A Multivariate Polynomial Approach](https://mlsanity.com/paper/2502.02209/) (2025)
- [Multilingual State Space Models for Structured Question Answering in Indic Languages](https://mlsanity.com/paper/2502.01673/) (2025)
- [Recall with Reasoning: Chain-of-Thought Distillation for Mamba's Long-Context Memory and Extrapolation](https://mlsanity.com/paper/2505.03320/) (2025)
- [xLSTM Scaling Laws: Competitive Performance with Linear Time-Complexity](https://mlsanity.com/paper/2510.02228/) (2025)
- [Emergence of Primacy and Recency Effect in Mamba: A Mechanistic Point of View](https://mlsanity.com/paper/2506.15156/) (2025)
- [Differential Mamba](https://mlsanity.com/paper/2507.06204/) (2025)
- [Predicting Training Re-evaluation Curves Enables Effective Data Curriculums for LLMs](https://mlsanity.com/paper/2509.25380/) (2025)
- [Predicate Renaming via Large Language Models](https://mlsanity.com/paper/2510.25517/) (2025)

## Abstract (arXiv)

In this technical report, we present Falcon Mamba 7B, a new base large language model based on the novel Mamba architecture. Falcon Mamba 7B is trained on 5.8 trillion tokens with carefully selected data mixtures. As a pure Mamba-based model, Falcon Mamba 7B surpasses leading open-weight models based on Transformers, such as Mistral 7B, Llama3.1 8B, and Falcon2 11B. It is on par with Gemma 7B and outperforms models with different architecture designs, such as RecurrentGemma 9B and RWKV-v6 Finch 7B/14B. Currently, Falcon Mamba 7B is the best-performing Mamba model in the literature at this scale, surpassing both existing Mamba and hybrid Mamba-Transformer models, according to the Open LLM Leaderboard. Due to its architecture, Falcon Mamba 7B is significantly faster at inference and requires substantially less memory for long sequence generation. Despite recent studies suggesting that hybrid Mamba-Transformer models outperform pure architecture designs, we demonstrate that even the pure Mamba design can achieve similar, or even superior results compared to the Transformer and hybrid designs. We make the weights of our implementation of Falcon Mamba 7B publicly available on https://huggingface.co/tiiuae/falcon-mamba-7b, under a permissive license.