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).
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:
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
