RWKV7-G1 "GooseOne" pure RNN reasoning model
These are BASE models (pretrained with web/code/synthetic + instruction/chat/reasoning data), suitable for post-training and fine-tuning (check https://huggingface.co/spaces/Jellyfish042/UncheatableEval to see their performance at language modeling).
More info & Gradio demo: https://rwkv.com/
Search "RWKV Chat" in play store / app store for our local inference app
RWKV Chat: https://rwkv.halowang.cloud/ (local inference for mobile/desktop) and https://github.com/RWKV-APP/RWKV_APP
Safetensor: https://huggingface.co/RWKV
GGUF: https://huggingface.co/collections/shoumenchougou/rwkv7-gxx-gguf
More GGUF & mobile models: https://huggingface.co/HaloWang/rwkv-weights (new) and https://huggingface.co/mollysama/rwkv-mobile-models (old)
Ollama GGUF: https://ollama.com/shoumenchougou (new) and https://ollama.com/mollysama (old)
RWKV-7 pth => GGUF script: https://github.com/MollySophia/rwkv-mobile/blob/master/converter/convert_rwkv_pth_to_gguf.py
Training: https://github.com/BlinkDL/RWKV-LM and https://github.com/Joluck/RWKV-PEFT
Note: rwkv7a has DeepEmbed
Efficient inference: https://github.com/BlinkDL/Albatross
- 145+ token/s RWKV-7 7.2B fp16 bsz1 decoding @ RTX5090 (always const speed & vram)
- 10250+ token/s RWKV-7 7.2B fp16 bsz960 decoding @ RTX5090 (always const speed & vram)
- 9650+ token/s RWKV-7 7.2B fp16 bsz320 decoding @ RTX5090 (always const speed & vram)
- 11289 token/s RWKV-7 7.2B fp16 bsz1 prefill @ RTX5090 (always const speed & vram)
pip inference: https://pypi.org/project/rwkv/
mobile inference: https://github.com/MollySophia/rwkv-mobile
There should not be any space at the end of your input (so strip your prompt) or you will upset the tokenizer and see non-English reponse.
Latest Best Practices for agentic work (study these projects): https://agent.objects.rwkvos.com/en
Eval example: https://github.com/BlinkDL/Albatross/blob/main/faster3a_2605/eval_gpqa_diamond.py
PROMPT GUIDE (including function call & agent): https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v7/RWKV7-G1x-templates.txt
Function call: temp 0, topp 0, penalty 0, works for RWKV-7 G1f 1.5B and larger models:
System: Tools:
- get_weather(location: string, unit?: "celsius" | "fahrenheit")
- get_stock_price(ticker: string)
- translate_text(text: string, target_language: string)
Return only a JSON function call.
User: Translate "Will it rain tomorrow?" into Japanese.
Assistant: ```json
and
System: Tools:
[
{"name":"find_free_slots","description":"Find free calendar slots","arguments":{"date":{"type":"string"},"duration_minutes":{"type":"integer"},"time_window":{"type":"string"}}},
{"name":"create_calendar_event","description":"Create a calendar event","arguments":{"title":{"type":"string"},"start_time":{"type":"string"},"end_time":{"type":"string"},"attendees":{"type":"array","items":{"type":"string"}}}}
]
Return only a JSON function call.
User: Schedule a 30-minute sync with Bob on 2026-05-08 afternoon.
Assistant: ```json
{"name":"find_free_slots","arguments":{"date":"2026-05-08","duration_minutes":30,"time_window":"afternoon"}}
```
User: Function output:
{"free_slots":[{"start":"2026-05-08T15:00:00+09:00","end":"2026-05-08T15:30:00+09:00"}],"bob_email":"bob@example.com"}
Assistant: ```json
The key is to keep it concise. Here you can enable "<think>" for Assistant too.
You can also use this template, which is closer to training data:
Assistant: <think>
...
</think>
<tool_call>
{...}
</tool_call>
<tool_call>
{...}
</tool_call>
User: <tool_response>
{...}
</tool_response>
<tool_response>
{...}
</tool_response>
Please always use latest models because they are always better at everything.
Decoding Suggestion (note: this is for RWKV pip pkg, which apply temp after topp):
Chat: temp 1, topp 0.5, alpha_presence 2, alpha_frequency 0.1, alpha_decay 0.99
Creative (great for fiction etc.): temp 0.6, topp 0.6 ~ 0.8, alpha_presence 2, alpha_frequency 0.2, alpha_decay 0.99
Chat prompt (note: better replace all \n\n in USER_PROMPT to \n as i am using \n\n as "chat round separator" in pretrain data):
System: YOU_CAN_USE_SYSTEM_IF_NEEDED
User: PREVIOUS_STUFF
Assistant: PREVIOUS_STUFF
User: USER_PROMPT
Assistant:
Think prompt (for hard prompts):
User: USER_PROMPT
Assistant: <think
Fake think prompt (great result, highly recommended):
User: USER_PROMPT
Assistant: <think></think
Think prompt, alternative style, for G1c and newer models. Note there is a space before the "(think)" after USER_PROMPT:
User: USER_PROMPT (think)
Assistant: <think
Shorter think, same style:
User: USER_PROMPT (think a bit)
Assistant: <think
Longer think, same style:
User: USER_PROMPT (think a lot)
Assistant: <think
FIM prompt (for G1c and newer models, works for text & code & everything):
✿prefix✿When I was young, I only liked to✿suffix✿and that’s how first I got interested in AI research.✿middle✿
Better (recommended):
✿prefix✿✿suffix✿and that’s how first I got interested in AI research.✿middle✿When I was young, I only liked to
Note "✿" will always be tokenized to one single token in RWKV tokenizer, so I picked it.
0.1B = L12-D768
0.4B = L24-D1024
1.5B = L24-D2048
2.9B = L32-D2560
7.2B = L32-D4096
13.3B = L61-D4096
Vocab = 65536 for all current models
Head size = 64 for all current models
Gxx = Data Version
G0x = less than 1 epoch, as training 1 epoch for a large model is expensive :(
G0 G0a G0a2 G0a3 ... G0b ... = adding more (newer and better) data, so G0a has better quality (but less) data than G1
G1x = more than 1 epoch
G1 G1a G1a2 G1a3 ... G1b ... = adding more (newer and better) data, note G1a has better quality (and more) data than G0a