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docs: fix env processor code fences and minor doc errors (#3953)

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* docs: fix code fences in env processor guide

The "Flexibility and Reusability" section wrapped a duplicated example
in a four-backtick fence and left a following block unclosed, so the
stray closing fence matched a later block. Everything in between
rendered as one code block that swallowed the surrounding prose.

Remove the duplicated block, add the missing closing fence after the
first example, and normalize the four-backtick fences to three so all
fences pair correctly.

* docs(pi0fast): fix typo 40kk -> 40k steps

* docs(integrate-hardware): fix so101 follower source link

* docs(hope_jr): fix dataset example link

The "example" link in the Record section pointed at the dataset's
`/settings` page, which returns HTTP 403 for readers. Drop the
`/settings` suffix so it links to the public dataset page the
sentence describes.

* docs(lekiwi): render emoji shortcodes as unicode

MDX does not expand `🤗` / `🤖` shortcodes, so they showed as
literal text in the rendered install step. Replace them with the 🤗 and
🤖 unicode characters, matching how the other robot pages write emoji.

* docs(smolvla): anchor record link to its section

The "Record a dataset" link dropped readers at the top of the
il_robots page instead of the relevant section. Point it at the
`#record-a-dataset` anchor (the `## Record a dataset` heading in
il_robots.mdx) so the link lands on the step it names.
This commit is contained in:
HUANG TZU-CHUN
2026-07-30 16:53:27 +08:00
committed by GitHub
parent 36b8face98
commit a6b06eac38
6 changed files with 7 additions and 20 deletions
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docs/source/pi0fast.mdx
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@@ -174,7 +174,7 @@ The model takes images, text instructions, and robot state as input, and outputs
## Reproducing π₀Fast results
We reproduce the results of π₀Fast on the LIBERO benchmark using the LeRobot implementation. We take the LeRobot PiFast base model [lerobot/pi0fast-base](https://huggingface.co/lerobot/pi0fast-base) and finetune for an additional 40kk steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).
We reproduce the results of π₀Fast on the LIBERO benchmark using the LeRobot implementation. We take the LeRobot PiFast base model [lerobot/pi0fast-base](https://huggingface.co/lerobot/pi0fast-base) and finetune for an additional 40k steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).
The finetuned model can be found here: