recursal

Eagle 7B is trained on 1.1 Trillion Tokens across 100+ world languages (70% English, 15% multilang, 15% code). - Built on the [RWKV-v5](/models?q=rwkv) architecture (a linear transformer with 10-100x+ lower inference cost) - Ranks as the world's greenest 7B model (per token) - Outperforms all 7B class models in multi-lingual benchmarks - Approaches Falcon (1.5T), LLaMA2 (2T), Mistral (>2T?) level of performance in English evals - Trade blows with MPT-7B (1T) in English evals - All while being an ["Attention-Free Transformer"](https://www.isattentionallyouneed.com/) Eagle 7B models are provided for free, by [Recursal.AI](https://recursal.ai), for the beta period till end of March 2024 Find out more [here](https://blog.rwkv.com/p/eagle-7b-soaring-past-transformers) [rnn](/models?q=rwkv)

This is an [RWKV 3B model](/models/rwkv/rwkv-5-world-3b) finetuned specifically for the [AI Town](https://github.com/a16z-infra/ai-town) project. [RWKV](https://wiki.rwkv.com) is an RNN (recurrent neural network) with transformer-level performance. It aims to combine the best of RNNs and transformers - great performance, fast inference, low VRAM, fast training, "infinite" context length, and free sentence embedding. RWKV 3B models are provided for free, by Recursal.AI, for the beta period. More details [here](https://substack.recursal.ai/p/public-rwkv-3b-model-via-openrouter). #rnn