RWKV v6 Finch 14B

RWKV v6 Finch 14B is an open-source large model that efficiently processes long texts.

PremiumNewProductProductivityArtificial IntelligenceNatural Language Processing

RWKV v6 Finch 14B is the sixth version of the RWKV architecture and the largest model in the series. By introducing data dependencies into token shifts and time-mixing, it enhances efficiency in processing long texts. The Finch 14B model better manages its long-term memory when handling prompts, offering a broader range of applications. This model is open-source, recognized by the Linux Foundation, and accepts community contributions of GPU cluster time to support training efforts.

The Finch 14B model features enhanced data processing capabilities with improved long-term memory management.
It continues the training based on the Eagle 7B model
stacking two 7B models to increase short-term memory.
Extensive benchmark testing has been conducted to assess model performance
including evaluations on the Open LLM Leaderboard v1.
Finch 7B improves upon Eagle 7B by 5.38% across all benchmarks
while Finch 14B adds an additional 7.14% improvement.
The model was trained on a dataset of 1.42 trillion tokens
demonstrating that its depth has not saturated.
The RWKV project receives donations of GPU cluster time to support further training and development.

RWKV v6 Finch 14B is ideal for researchers and developers who need to handle large volumes of text data
especially in the fields of natural language processing and machine learning. Its efficiency and open-source nature make it a perfect choice for advancing AI research and applications.

For developing multilingual chatbots.
Conducting sentiment analysis on large-scale text datasets.
Assisting in translation and text generation tasks as a language model.

Visit the RWKV GitHub page to download the model weights and training code.
Set up and configure the necessary hardware and software environment according to the provided guidelines.
Utilize the provided inference service for model testing or directly integrate it into applications.
Participate in community discussions and contribute GPU cluster time for the model's training and development.
Fine-tune and optimize the model based on project requirements.

Visit

RWKV v6 Finch 14B Visit Over Time

Monthly Visits

150

Bounce Rate

0.00%

Page per Visit

6.0

Visit Duration

00:00:29

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RWKV v6 Finch 14B

RWKV v6 Finch 14B Visit Over Time

RWKV v6 Finch 14B Visit Trend

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