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agentic-ptb/kimi.h016.rl_sharedterm.step_30
kimi.h016.rl_sharedterm.step_30 is a machine learning model from agentic-ptb. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
AgentPTB sweep checkpoint. Cell kimi — kimi-code / kimi-k3 @ effort high.
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From the Hugging Face model README
AgentPTB sweep checkpoint. Cell kimi — kimi-code / kimi-k3 @ effort high.
| field | value |
|---|---|
| plot cell | kimi |
| driver | kimi-code / kimi-k3 |
| reasoning effort | high |
| run boot (UTC) | 2026-08-15T21:03:17Z |
| role | intermediate |
| hours into run | h17.11 of 100 |
| checkpoint path in run | runs/rl_sharedterm/weights/step_60 |
| shards | 4 |
| size | 18.8 GB |
| base model | Qwen/Qwen3.5-9B-Base |
| eos_token_id | [248044] ⚠️ MISSING 248046 |
248046 is <|im_end|>, the token the Qwen3.5 chat template ends every assistant turn with.
Checkpoints missing it do not stop at end-of-turn and overrun the context window, so their
eval numbers are a floor, not a measurement — compare them only against other checkpoints
with the same eos status, or re-package before evaluating.
The repo id is {cell}.h{HHH}.{family}.{step}, where hHHH is the hour of the 100-hour run
at which this checkpoint was written — the same x-axis the sweep figures use for eval
panels (t_h). So a checkpoint drops onto the performance-over-time curve directly, and
sorting repo ids within a cell sorts them chronologically.
hHHH is rounded down to whole hours for sortability; the exact value is the
hours into run row above, and in agentic-ptb/INDEX.