RWKV-6 Finch 7B
RWKV-6 Finch 7B is a released long context model with open-source and Infinite context; evaluate it while provider pricing coverage matures.
Use it for
- Teams evaluating long context
- Workloads that can use a Infinite context window
Do not use it for
- Cost-sensitive launches that need sourced token pricing
- Vision or document-understanding workloads
- Strict JSON or tool-calling flows
- Family
- RWKV-6 Finch
- Released
- 2024-04-09
- Context
- Infinite
- Parameters
- 7B
- Architecture
- Decoder Only
- Specialization
- general
- Openness
- Open source
- License
- Apache 2.0OSI-approvedCommercial use: permitted
- Weights
- Available
- Code
- Unknown
- Training
- Pretrained
Linear complexity language models combining the efficiency of RNNs with the parallelism of Transformers
No tracked provider token pricing is available yet.
About
RWKV-6 Finch 7B is a flagship mid-size model from the RWKV-6 architecture series. Introduced alongside the Eagle and Finch paper (arXiv 2404.05892, April 2024). The Finch 14B model was subsequently derived by stacking two Finch 7B weights. Uses multi-headed matrix-valued states for improved language comprehension. Constant-memory inference. Apache 2.0 licensed.
RWKV-6 Finch 7B is an open-source model in the RWKV-6 Finch family. The structured metadata tracks a Infinite context window. No headline benchmark score is tracked for RWKV-6 Finch 7B yet.
Top use-case fit
Long context
Included by capability and metadata signals in the decision map.
Provider price ladder
No tracked provider token pricing is available for this model yet.
Capabilities
No model capability flags are currently sourced.
Benchmark peer barsfor Long context
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.
Rankings & picks(2)
Frequently asked questions
What is the context window of RWKV-6 Finch 7B?
RWKV-6 Finch 7B has a context window of Infinite tokens.
When was RWKV-6 Finch 7B released?
RWKV-6 Finch 7B was released on 2024-04-09.
Linear complexity language models combining the efficiency of RNNs with the parallelism of Transformers
No tracked provider token pricing is available yet.