fix assembly instructions typo

docs/source/annotation_pipeline.mdx

@@ -81,12 +81,6 @@ merged. Both prompts also carry a causal **event-boundary** definition (a
 new event starts when an object becomes held / is released / reaches a new
 location / a lid changes state / contents move) to sharpen where cuts land.
 
-Optionally, a third **seeded-relabel** pass (`--plan.subtask_seeded_relabel`)
-revisits each span with its previous/current/next segment contact sheets and
-minimally corrects the label, using the first label as a prior — it keeps the
-boundaries fixed and only sharpens wording, at the cost of one extra call per
-subtask.
-
 The resulting spans are then stitched into a gap-free, full-episode
 cover, so **every frame has exactly one active subtask**. See
 [`run_hf_job.py`](https://github.com/huggingface/lerobot/blob/main/examples/annotations/run_hf_job.py)
@@ -163,33 +157,30 @@ Every module is on by default and can be toggled independently (set to
 
 ### The VLM (`--vlm.*`)
 
-| Flag                       | Default            | What it does                                                                         |
-| -------------------------- | ------------------ | ------------------------------------------------------------------------------------ |
-| `--vlm.model_id`           | `Qwen/Qwen3.6-27B` | The model to serve and prompt.                                                       |
-| `--vlm.camera_key`         | first `images.*`   | Which camera every prompt is grounded on.                                            |
-| `--vlm.serve_command`      | auto               | The exact `vllm serve …` command (set TP size, GPU memory, `--max-model-len` here).  |
-| `--vlm.parallel_servers`   | `1`                | Independent servers for round-robin routing (one per GPU).                           |
-| `--vlm.num_gpus`           | `0`                | GPUs per server (`0` = one each).                                                    |
-| `--vlm.client_concurrency` | `16`               | In-flight requests across all servers.                                               |
-| `--vlm.max_new_tokens`     | `512`              | Generation cap per call.                                                             |
-| `--vlm.temperature`        | `0.2`              | Sampling temperature.                                                                |
-| `--vlm.reasoning_effort`   | `null`             | Thinking-budget hint (`low`/`medium`/`high`) forwarded to OpenAI-compatible servers. |
+| Flag                       | Default            | What it does                                                                        |
+| -------------------------- | ------------------ | ----------------------------------------------------------------------------------- |
+| `--vlm.model_id`           | `Qwen/Qwen3.6-27B` | The model to serve and prompt.                                                      |
+| `--vlm.camera_key`         | first `images.*`   | Which camera every prompt is grounded on.                                           |
+| `--vlm.serve_command`      | auto               | The exact `vllm serve …` command (set TP size, GPU memory, `--max-model-len` here). |
+| `--vlm.parallel_servers`   | `1`                | Independent servers for round-robin routing (one per GPU).                          |
+| `--vlm.num_gpus`           | `0`                | GPUs per server (`0` = one each).                                                   |
+| `--vlm.client_concurrency` | `16`               | In-flight requests across all servers.                                              |
+| `--vlm.max_new_tokens`     | `512`              | Generation cap per call.                                                            |
+| `--vlm.temperature`        | `0.2`              | Sampling temperature.                                                               |
 
 ### Subtasks / plan / memory (`--plan.*`)
 
-| Flag                            | Default    | What it does                                                                                                                 |
-| ------------------------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------- |
-| `--plan.frames_per_second`      | `2.0`      | Frame sampling rate for the contact sheets (`2.0` = one frame every 0.5s).                                                   |
-| `--plan.max_frames_per_prompt`  | `60`       | Frame budget per VLM call. Episodes whose sampling exceeds this are auto-windowed at the same density, then stitched.        |
-| `--plan.contact_sheet_columns`  | `5`        | Columns per contact-sheet grid (`contact_sheet_frames_per_sheet` tiles, time row-major).                                     |
-| `--plan.plan_max_steps`         | `8`        | Upper bound on subtasks per episode.                                                                                         |
-| `--plan.subtask_describe_first` | `true`     | Run the describe→segment grounding pass (best subtask quality; +1 call/episode).                                             |
-| `--plan.subtask_seeded_relabel` | `false`    | Second pass: re-label each subtask from its prev/current/next contact sheets, seeded with the first label (+1 call/subtask). |
-| `--plan.subtask_relabel_frames` | `5`        | Frames sampled uniformly per segment sheet in the relabel pass (only used when `subtask_seeded_relabel=true`).               |
-| `--plan.emit_plan`              | `true`     | Emit the numbered `plan` rows (`false` = subtasks + memory only).                                                            |
-| `--plan.emit_memory`            | `true`     | Emit the `memory` rows (`false` = subtasks + plan only); symmetric to `emit_plan`.                                           |
-| `--plan.n_task_rephrasings`     | `10`       | How many `task_aug` rephrasings to emit (`0` disables).                                                                      |
-| `--plan.derive_task_from_video` | `if_short` | Use the dataset task as-is (`off`), only when it's missing/short (`if_short`), or always re-derive from video (`always`).    |
+| Flag                            | Default    | What it does                                                                                                              |
+| ------------------------------- | ---------- | ------------------------------------------------------------------------------------------------------------------------- |
+| `--plan.frames_per_second`      | `2.0`      | Frame sampling rate for the contact sheets (`2.0` = one frame every 0.5s).                                                |
+| `--plan.max_frames_per_prompt`  | `60`       | Frame budget per VLM call. Episodes whose sampling exceeds this are auto-windowed at the same density, then stitched.     |
+| `--plan.contact_sheet_columns`  | `5`        | Columns per contact-sheet grid (`contact_sheet_frames_per_sheet` tiles, time row-major).                                  |
+| `--plan.plan_max_steps`         | `8`        | Upper bound on subtasks per episode.                                                                                      |
+| `--plan.subtask_describe_first` | `true`     | Run the describe→segment grounding pass (best subtask quality; +1 call/episode).                                          |
+| `--plan.emit_plan`              | `true`     | Emit the numbered `plan` rows (`false` = subtasks + memory only).                                                         |
+| `--plan.emit_memory`            | `true`     | Emit the `memory` rows (`false` = subtasks + plan only); symmetric to `emit_plan`.                                        |
+| `--plan.n_task_rephrasings`     | `10`       | How many `task_aug` rephrasings to emit (`0` disables).                                                                   |
+| `--plan.derive_task_from_video` | `if_short` | Use the dataset task as-is (`off`), only when it's missing/short (`if_short`), or always re-derive from video (`always`). |
 
 ### Interjections + VQA
 

docs/source/lingbot_va.mdx

@@ -111,26 +111,17 @@ Requirements:
 - The block-causal masks use PyTorch **flex-attention**, so build the policy with
   `--policy.attn_mode=flex` for training (the default `torch` SDPA is inference-only).
 - The full 5B DiT does not fit a single 24–32 GB GPU under AdamW; fine-tune with **LoRA**
-  (`--peft.method_type=LORA`) and/or optimizer offload. `get_optim_params` returns only the
-  trainable (e.g. adapter) parameters; the VAE + UMT5 text encoder stay frozen. Install the
-  `lerobot[peft]` extra to enable PEFT support (see the [PEFT training guide](./peft_training)).
+  (`--policy.use_peft=true`) and/or optimizer offload. `get_optim_params` returns only the
+  trainable (e.g. adapter) parameters; the VAE + UMT5 text encoder stay frozen.
 
 ```bash
 lerobot-train \
   --policy.path=lerobot/lingbot_va_libero_long --policy.attn_mode=flex \
-  --peft.method_type=LORA --peft.r=32 --peft.lora_alpha=32 \
-  --peft.target_modules='transformer\.blocks\.\d+\.attn[12]\.(to_q|to_v)' \
+  --policy.use_peft=true \
   --dataset.repo_id=<your LeRobot-format dataset> \
   --batch_size=1 --steps=... --output_dir=outputs/train/lingbot_va
 ```
 
-Unlike SmolVLA / π₀, LingBot-VA does not ship built-in default LoRA targets, so you must pass
-`--peft.target_modules` explicitly. Only `self.transformer` (the dual-stream Wan transformer) is
-trainable; the example above adapts the query/value projections of both its self-attention
-(`attn1`) and cross-attention (`attn2`) blocks — the standard LoRA target set. Broaden it (e.g.
-add `to_k`/`to_out`, or the `ffn` layers) if you need a higher-capacity adapter. Passing
-`--peft.method_type` implies PEFT, so `--policy.use_peft=true` is not required.
-
 The dataset must provide camera clips (a temporal window per camera, VAE-encoded to
 `frame_chunk_size` latent frames) and `frame_chunk_size * action_per_frame` action steps per item.
 

docs/source/peft_training.mdx

@@ -62,10 +62,3 @@ to the `--peft.full_training_modules` parameter:
 
 The learning rate and the scheduled target learning rate can usually be scaled by a factor of 10 compared to the
 learning rate used for full fine-tuning (e.g., 1e-4 normal, so 1e-3 using LoRA).
-
-## Other policies
-
-The same `--peft.*` flags work for any pre-trained policy. Some policies (SmolVLA, π₀, π₀.₅) ship
-built-in default `target_modules`, so `--peft.method_type=LORA` is enough. Others do not, and will
-ask you to pass `--peft.target_modules` explicitly — for example LingBot-VA, whose recommended
-targets are documented in its [dedicated guide](./lingbot_va#training--fine-tuning).

docs/source/so101.mdx

@@ -338,7 +338,7 @@ It is advisable to install one 3-pin cable in the motor after placing them befor
 <hfoption id="Leader">
 
 - Mount the leader holder onto the wrist and secure it with 4 M3x6mm screws.
-- Attach the handle to motor 5 using 1 M2x6mm screw.
+- Attach the handle to the leader holder using 1 M2x6mm screw.
 - Insert the gripper motor, secure it with 2 M2x6mm screws on each side, attach a motor horn using a M3x6mm horn screw.
 - Attach the follower trigger with 4 M3x6mm screws.
 

examples/annotations/run_hf_job.py

@@ -46,11 +46,8 @@ CMD = (
     "apt-get update -qq && apt-get install -y -qq git ffmpeg && "
     "pip install --no-deps "
     "'lerobot @ git+https://github.com/huggingface/lerobot.git@main' && "
-    # Pins mirror pyproject.toml — unpinned installs pull av 18 / datasets 5 /
-    # draccus 0.11, which break lerobot at import time.
     "pip install --upgrade-strategy only-if-needed "
-    "'datasets>=4.7.0,<5.0.0' 'pyarrow>=21.0.0,<30.0.0' 'av>=15.0.0,<16.0.0' 'draccus==0.10.0' "
-    "'pandas>=2.0.0,<3.0.0' jsonlines gymnasium torchcodec mergedeep pyyaml-include toml typing-inspect "
+    "datasets pyarrow av jsonlines draccus gymnasium torchcodec mergedeep pyyaml-include toml typing-inspect "
     "openai && "
     "export VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0 && "
     "export VLLM_VIDEO_BACKEND=pyav && "

src/lerobot/annotations/steerable_pipeline/config.py

@@ -65,14 +65,6 @@ class PlanConfig:
     # invented from the task text (+1 VLM call/episode).
     subtask_describe_first: bool = True
 
-    # Seeded relabeling: after segmentation, re-label each span with a focused
-    # pass that sees the previous / current / next segment contact sheets and
-    # minimally corrects the seed label (macrodata's best end-to-end labeling
-    # step). Costs +1 VLM call per subtask; off by default.
-    subtask_seeded_relabel: bool = False
-    # Frames sampled uniformly per segment sheet in the relabel pass.
-    subtask_relabel_frames: int = 5
-
     # Emit ``style="plan"`` rows at each boundary; False = subtasks + memory only.
     emit_plan: bool = True
 
@@ -168,11 +160,6 @@ class VlmConfig:
     # Forwarded as extra_body.chat_template_kwargs (e.g. {"enable_thinking": false}).
     chat_template_kwargs: dict[str, Any] | None = None
 
-    # OpenAI-style thinking budget hint ("low"/"medium"/"high"); forwarded to
-    # the server when set. Used to cap a thinking model's reasoning so it
-    # leaves tokens for the actual JSON answer on OpenAI-compatible endpoints.
-    reasoning_effort: str | None = None
-
 
 @dataclass
 class ExecutorConfig:

src/lerobot/annotations/steerable_pipeline/frames.py

@@ -413,16 +413,7 @@ def _draw_timestamp_badge(image: PIL.Image.Image, timestamp: float) -> PIL.Image
 
     result = image.copy()
     draw = ImageDraw.Draw(result)
-    # Scale the timestamp to the tile so it stays legible after the model
-    # downsamples the full sheet into 768px tiles — a tiny bitmap font blurs
-    # at contact-sheet resolution and the VLM can no longer read the exact
-    # source time, which is what the boundary score depends on. ``size=`` is
-    # supported by Pillow's bitmap default since 10.1; fall back otherwise.
-    badge_px = max(14, round(image.height * 0.12))
-    try:
-        font = ImageFont.load_default(size=badge_px)
-    except TypeError:
-        font = ImageFont.load_default()
+    font = ImageFont.load_default()
     label = f"{timestamp:06.2f}s"
     left, top, right, bottom = draw.textbbox((0, 0), label, font=font)
     text_w, text_h = right - left, bottom - top

src/lerobot/annotations/steerable_pipeline/modules/plan_subtasks_memory.py

@@ -116,8 +116,6 @@ class PlanSubtasksMemoryModule:
             rows.extend(self._task_aug_rows([effective_task, *variants], t0))
 
         subtask_spans = self._generate_subtasks(record, task=effective_task)
-        if self.config.subtask_seeded_relabel and subtask_spans:
-            subtask_spans = self._seeded_relabel(record, subtask_spans, effective_task)
 
         # subtask rows
         for span in subtask_spans:
@@ -511,51 +509,6 @@ class PlanSubtasksMemoryModule:
 
         return cleaned
 
-    def _seeded_relabel(
-        self, record: EpisodeRecord, spans: list[dict[str, Any]], task: str
-    ) -> list[dict[str, Any]]:
-        """Re-label each span using prev/current/next segment contact sheets.
-
-        Boundaries are kept fixed; only ``text`` is refined. The original
-        ("seed") label is passed as a strong prior so the model verifies and
-        minimally corrects it rather than re-describing from scratch — the
-        macrodata seeded-relabeling step. One VLM call per span.
-        """
-        n = len(spans)
-        out: list[dict[str, Any]] = []
-        for i, span in enumerate(spans):
-            content: list[dict[str, Any]] = []
-            if i > 0:
-                content += self._segment_sheet(record, spans[i - 1])
-            content += self._segment_sheet(record, span)
-            if i < n - 1:
-                content += self._segment_sheet(record, spans[i + 1])
-            prompt = load_prompt("plan_subtask_relabel").format(
-                episode_task=task,
-                seed_label=span["text"],
-                segment_index=i + 1,
-                segment_count=n,
-                start=float(span["start"]),
-                end=float(span["end"]),
-            )
-            content.append({"type": "text", "text": prompt})
-            label = self._vlm_field([{"role": "user", "content": content}], "label")
-            text = label.strip() if isinstance(label, str) and label.strip() else span["text"]
-            out.append({**span, "text": text})
-        return out
-
-    def _segment_sheet(self, record: EpisodeRecord, span: dict[str, Any]) -> list[dict[str, Any]]:
-        """Contact-sheet block(s) for one span: up to N frames sampled uniformly."""
-        s, e = float(span["start"]), float(span["end"])
-        n = max(1, int(self.config.subtask_relabel_frames))
-        if e <= s or n == 1:
-            timestamps = [s]
-        else:
-            step = (e - s) / (n - 1)
-            timestamps = [s + i * step for i in range(n)]
-        frames = self.frame_provider.frames_at(record, timestamps)
-        return self._contact_sheet_blocks(frames, timestamps[: len(frames)])
-
     def _generate_subtasks_windowed(
         self, record: EpisodeRecord, task: str, window_s: float
     ) -> list[dict[str, Any]]:

src/lerobot/annotations/steerable_pipeline/prompts/__init__.py

@@ -22,23 +22,12 @@ plain editors and roundtrip cleanly through ``ruff format``.
 
 from __future__ import annotations
 
-import os
 from pathlib import Path
 
 _DIR = Path(__file__).parent
 
 
 def load(name: str) -> str:
-    """Read prompt template ``name.txt`` from the ``prompts/`` directory.
-
-    A ``LEROBOT_PROMPT_OVERRIDE_<name>`` environment variable, when set to a
-    non-empty value, takes precedence over the packaged file. This lets prompt
-    search (e.g. GEPA) inject candidate templates into a remote job without
-    rebuilding the package; the override must keep the same ``{placeholder}``
-    fields the call site formats in.
-    """
-    override = os.environ.get(f"LEROBOT_PROMPT_OVERRIDE_{name}")
-    if override and override.strip():
-        return override
+    """Read prompt template ``name.txt`` from the ``prompts/`` directory."""
     path = _DIR / f"{name}.txt"
     return path.read_text(encoding="utf-8")

src/lerobot/annotations/steerable_pipeline/prompts/plan_subtask_relabel.txt

@@ -1,35 +0,0 @@
-Annotate one fixed segment from a longer robot demonstration.
-
-Return only JSON:
-  {{"label": "<short descriptive subtask label>"}}
-
-You are shown up to three timestamped contact sheets, in order:
-- The FIRST sheet is the PREVIOUS segment (context only); it may be absent.
-- The SECOND sheet is the CURRENT target segment.
-- The THIRD sheet is the NEXT segment (context only); it may be absent.
-Each tile has its timestamp (seconds, absolute video time) burned into its
-top-left corner.
-
-Episode instruction: "{episode_task}"
-Target segment: {segment_index} of {segment_count}
-Target time: {start:.2f}s to {end:.2f}s
-Original predicted label for this exact segment: "{seed_label}"
-
-Rules:
-- Label ONLY the current target segment (the second sheet). Use the
-  previous/next sheets only to disambiguate what changed.
-- Treat the original predicted label as a STRONG PRIOR, not ground truth:
-  verify it against the current segment and correct it minimally.
-- If it already names the right action and main object, keep it; only fix
-  grammar or add a clearly visible essential detail.
-- If it is vague but directionally correct, make it more specific.
-- If it describes the previous/next segment, the wrong action, wrong
-  object, wrong destination, or a wrong state change, replace it.
-- Do not describe the previous or next segment, and do not split, merge,
-  or move the fixed segment.
-- Do not introduce an action that is not clearly visible in the current
-  target segment.
-- Use one concise imperative phrase. Name the manipulated object and the
-  action / state change. Include source, destination, side, direction,
-  final placement, or opened/closed state when visible and central.
-- Do not mention timestamps, frame numbers, uncertainty, or intent.

src/lerobot/annotations/steerable_pipeline/prompts/plan_subtasks.txt

@@ -1,68 +1,112 @@
-You are annotating a teleoperated robot demonstration shown as
-timestamped contact sheets (each tile has its time in seconds burned
-into the top-left corner). The operator's goal was: "{episode_task}"
+You are labeling a teleoperated robot demonstration.
 
-{observation_block}Reconstruct the sequence of COMPLETED manipulation events the robot
-performs, in chronological order. Output one segment per event with a
-[start, end] time in seconds and a short action label.
+The user originally asked: "{episode_task}"
 
-GROUNDING — read first, it overrides everything below:
-- Label ONLY events you can SEE in the frames. The instruction is the
-  goal; the VIDEO is the ground truth for what actually happened.
-- Do NOT invent, anticipate, or pad steps that are not shown.
+You are shown the entire demonstration as a single video. Watch the
+whole clip, then segment it into a list of consecutive atomic subtasks
+the robot performs.
 
-Granularity — segment by completed events, not by motion:
-- Start a NEW segment whenever the world state changes: an object is
-  grasped, lifted, transported, placed, or released; a held object
-  changes; a drawer/door/lid/container opens or closes; contents move
-  between containers (poured); a tool starts or stops acting on a
-  surface. Watch the gripper open/close transitions — they usually mark
-  boundaries.
-- Do NOT split approach, reach, grasp adjustment, small repositioning,
-  hesitation, or retreat into their own segments. Fold each into the
-  event it belongs to (the approach is part of the pick; the retreat is
-  part of the place).
-- Do NOT merge separate completed events. Each distinct pick, place,
-  open, close, pour, push, wipe, or insert is its own segment, even when
-  they repeat on different objects or locations.
-- Most segments last 2-10 seconds. Shorter segments are okay ONLY for
-  fast pick / place / open / close / release events. Never emit a
-  segment shorter than {min_subtask_seconds} seconds; merge a too-short
-  candidate into its neighbour instead.
-- Skip idle time, pure camera motion, and tiny hand jitter.
+{observation_block}GROUNDING — read this first, it overrides everything below:
+- Label ONLY what the robot actually does in the video. Every subtask
+  you emit must correspond to motion you can SEE in specific frames.
+- Do NOT invent, anticipate, or pad. If the robot only does one thing
+  (e.g. it just navigates to a location and the clip ends), emit
+  EXACTLY ONE subtask. Many demonstrations are a single atomic skill.
+- ``max_steps`` below is a hard CEILING, not a target. Emitting fewer
+  subtasks than the ceiling is not just allowed, it is expected for
+  short / atomic demonstrations. One correct subtask is far better
+  than several invented ones.
+- If the video does not clearly show the action implied by the task,
+  describe what you actually see — do NOT fabricate the task's steps
+  from the instruction text. The instruction tells you the goal; the
+  VIDEO is the ground truth for what happened.
 
-Labels — short imperative phrases:
-- One concise command naming the action and the manipulated object, e.g.
-  "pick up the red cup", "put the cup on the shelf", "open the top
-  drawer", "pour water into the glass", "insert the plug into the
-  socket".
-- Include source, destination, side, direction, or the final
-  open/closed state when it is visible and central to the event.
-- Prefer these verbs (extend only when none fits): pick up, put, place,
-  push, pull, turn, press, open, close, pour, insert, wipe, stack.
-  Disambiguate by what you SEE:
-    * STACK vs PUT: object placed ON TOP OF another object -> "stack".
-    * INSERT vs PUT: object pushed INTO a fitted slot/hole/socket -> "insert".
-    * PICK UP vs PUT (direction): gripper CLOSES and object moves WITH
-      the hand -> "pick up"; gripper OPENS and object stays -> "put".
-    * POUR vs PUT: source is tilted and contents flow -> "pour".
-- Use the exact object nouns implied by the task; stay consistent across
-  the episode (don't switch "cube" to "block").
-- Write imperative commands, never third person ("the robot ..."), and
-  drop articles/adverbs.
+Authoring rules — Hi Robot atom granularity, pi0.7-style short prompts:
 
-Timing:
-- Use the burned-in timestamps to set start and end. Boundaries should
-  land on or near a printed time, and every [start, end] must lie within
-  [0.0, {episode_duration}] seconds, be non-overlapping, and cover the
-  episode in order.
-- Emit at most {max_steps} segments.
+- Each subtask = one COMPOSITE atomic skill the low-level policy can
+  execute end-to-end. A "skill" bundles its own approach motion with
+  its terminal action — do NOT split the approach off as its own
+  subtask. The whole-arm policy already learns to reach as part of
+  every manipulation primitive.
+- Write each subtask as an IMPERATIVE COMMAND, starting with one of
+  these verbs (extend only when none fits):
+    pick up <obj>           — approach + grasp + lift in one subtask
+    put <obj> on/in <loc>   — transport + release in one subtask
+    place <obj> on/in <loc> — synonym of "put"; pick one and stay consistent
+    push <obj>              — contact + linear shove
+    pull <obj>              — contact + linear retract
+    turn <knob/dial/handle> — rotary actuation
+    press <button>          — single-press contact
+    open <drawer/door/lid>  — full open motion
+    close <drawer/door/lid> — full close motion
+    pour <src> into <dst>   — tilt + flow
+    insert <obj> into <slot>— alignment + push-fit
+    go to <loc>             — ONLY when no grasp / actuation follows
+                             (e.g. a pure relocation between phases).
+                             If the next subtask grasps something at
+                             that location, drop "go to ..." and just
+                             write "pick up ..." instead.
+- Forbidden ultra-fine splits — the VLM is NOT allowed to emit these
+  as standalone subtasks; fold them into the parent composite:
+    "move to X"   → fold into "pick up X" (or whatever follows)
+    "reach for X" → fold into "pick up X"
+    "grasp X"     → fold into "pick up X"
+    "lift X"      → fold into "pick up X" (or "put X on Y" if it's
+                    the transport phase of a place)
+    "release X"   → fold into "put X on Y" (or "place X in Y")
+- Keep it SHORT — a verb phrase, not a sentence. Drop articles
+  ("the", "a") and adverbs ("carefully", "slowly"). Add a "how"
+  detail (which hand, which grasp point) ONLY when it is needed to
+  disambiguate. Every subtask must begin with one of the verbs
+  above (no leading nouns, no "then", no "first").
+- NEVER use third person. Never write "the robot", "the arm", "the
+  gripper moves", "it picks up" — the robot is implied. Command it,
+  do not describe it.
+- Use the exact object nouns from the task above. If the task says
+  "cube", every subtask says "cube" — never switch to "block". If it
+  says "box", never switch to "bin"/"container". Keep vocabulary
+  consistent across the whole episode.
+- Good: "pick up blue cube", "put blue cube in box", "open drawer",
+  "turn red knob", "press start button", "go to sink".
+- Bad: "move to blue cube" (approach as its own subtask — forbidden,
+  must be folded into "pick up blue cube"); "the robot arm moves
+  towards the blue cube" (third person, too long); "carefully pick
+  up the cube" (adverb, article); "release the yellow block"
+  ("block" when the task said "cube", and "release" must be folded
+  into a "put"/"place" subtask).
+- Subtasks are non-overlapping and cover the full episode in order.
+  Choose the cut points yourself based on what you see in the video
+  (gripper open/close events, contact, regrasps, transitions).
+- Each subtask spans at least {min_subtask_seconds} seconds. If a
+  candidate span would be shorter, merge it into its neighbour
+  rather than emitting it.
+- Do not exceed {max_steps} subtasks total. Fewer, larger composites
+  are preferred over many micro-steps.
+- Every subtask's [start_time, end_time] must lie within
+  [0.0, {episode_duration}] seconds.
+
+SPECIAL CASES — verb disambiguation (each rule is narrowly visual and
+fires ONLY on the spatial situation it names; it must not change how you
+label any other situation):
+- STACK vs PUT: if an object is placed ON TOP OF another specific object
+  (not on a flat table / shelf / counter), use "stack ... on ...", not
+  "put". "stack blue book on green book", NOT "put blue book on table".
+- INSERT vs PUT: if an object goes INTO a fitted slot / hole / socket /
+  receptacle (push-fit), use "insert ... into ...", not "put".
+- RETRIEVE/PICK-UP vs PUT (direction): watch the gripper. If it CLOSES
+  on the object and the object moves WITH the hand, it is "pick up" /
+  "retrieve" (object leaves its location). If the gripper OPENS and the
+  object stays where the hand left it, it is "put" / "place" (object
+  arrives at a location). Decide by which way the object moves, not by
+  where the hand ends up.
+- POUR vs PUT: only use "pour" when the source is tilted and contents
+  flow out; moving a full container without tilting is "put"/"place".
 
 Output strictly valid JSON of shape:
 
   {{
     "subtasks": [
-      {{"text": "<short imperative action label>", "start": <float>, "end": <float>}},
+      {{"text": "<short imperative verb phrase>", "start": <float>, "end": <float>}},
       ...
     ]
   }}

src/lerobot/annotations/steerable_pipeline/vlm_client.py

@@ -285,8 +285,6 @@ def _make_openai_client(config: VlmConfig) -> VlmClient:
             "max_tokens": max_tok,
             "temperature": temp,
         }
-        if config.reasoning_effort:
-            kwargs["reasoning_effort"] = config.reasoning_effort
         extra_body: dict[str, Any] = {}
         if send_mm_kwargs and mm_kwargs:
             extra_body["mm_processor_kwargs"] = {**mm_kwargs, "do_sample_frames": True}
@@ -298,13 +296,7 @@ def _make_openai_client(config: VlmConfig) -> VlmClient:
             chosen = clients[rr_counter["i"] % len(clients)]
             rr_counter["i"] += 1
         response = chosen.chat.completions.create(**kwargs)
-        # Some OpenAI-compatible servers can return a choice with no message
-        # (safety filter, or a "thinking" model that spends the whole budget
-        # before emitting content). Treat that as an empty reply so the
-        # JSON-retry path handles it instead of crashing the run.
-        choice = response.choices[0] if response.choices else None
-        message = choice.message if choice is not None else None
-        return (message.content if message is not None else None) or ""
+        return response.choices[0].message.content or ""
 
     def _gen(batch: Sequence[Sequence[dict[str, Any]]], max_tok: int, temp: float) -> list[str]:
         if len(batch) <= 1 or config.client_concurrency <= 1:

src/lerobot/configs/train.py

@@ -221,11 +221,6 @@ class TrainPipelineConfig(HubMixin):
             )
 
         active_cfg = self.trainable_config
-
-        # Keep the policy-level `use_peft` flag in sync with the presence of a `--peft.*` config.
-        if self.peft is not None and self.policy is not None:
-            self.policy.use_peft = True
-
         if self.rename_map and active_cfg.pretrained_path is None:
             raise ValueError(
                 "`rename_map` requires a pretrained policy checkpoint. "

src/lerobot/policies/evo1/internvl3_embedder.py

@@ -27,11 +27,9 @@ from lerobot.utils.import_utils import _transformers_available, require_package
 
 if TYPE_CHECKING or _transformers_available:
     from transformers import AutoModel, AutoTokenizer
-    from transformers.utils import is_flash_attn_2_available
 else:
     AutoModel = None
     AutoTokenizer = None
-    is_flash_attn_2_available = None
 
 IMAGENET_MEAN = (0.485, 0.456, 0.406)
 IMAGENET_STD = (0.229, 0.224, 0.225)
@@ -137,13 +135,9 @@ class InternVL3Embedder(nn.Module):
                 raise ValueError(f"Unsupported EVO1 vlm_dtype '{model_dtype}'") from exc
         self.model_dtype = model_dtype
 
-        attn_implementation = (
-            "flash_attention_2" if (use_flash_attn and is_flash_attn_2_available()) else "eager"
-        )
+        attn_implementation = "flash_attention_2" if (use_flash_attn and _flash_attn_available()) else "eager"
         if use_flash_attn and attn_implementation == "eager":
-            logger.warning(
-                "Flash Attention 2 is unavailable on this runtime. Falling back to eager attention."
-            )
+            logger.warning("flash_attn is not installed. Falling back to eager attention.")
 
         self.model = AutoModel.from_pretrained(
             model_name,
@@ -365,3 +359,11 @@ class InternVL3Embedder(nn.Module):
     @property
     def device(self) -> torch.device:
         return next(self.model.parameters()).device
+
+
+def _flash_attn_available() -> bool:
+    try:
+        import flash_attn  # noqa: F401
+    except ModuleNotFoundError:
+        return False
+    return True

src/lerobot/policies/factory.py

@@ -513,37 +513,6 @@ def make_pre_post_processors(
     return processors
 
 
-def _has_peft_adapter_config(
-    pretrained_path: str,
-    revision: str | None = None,
-) -> bool:
-    """Return whether ``pretrained_path`` points to an existing PEFT adapter.
-
-    A PEFT adapter checkpoint always ships an ``adapter_config.json``.
-    A plain base-model checkpoint does not. This distinction lets us tell apart
-    two very different ``use_peft=True`` scenarios that both set ``pretrained_path``:
-
-    * loading/resuming a previously trained adapter (config lives at ``pretrained_path``)
-    * starting a *fresh* PEFT fine-tune on top of a base model
-
-    Works for both local directories and Hub repo ids.
-    """
-    import os
-
-    adapter_config_name = "adapter_config.json"
-
-    if os.path.isdir(pretrained_path):
-        return os.path.isfile(os.path.join(pretrained_path, adapter_config_name))
-
-    from huggingface_hub import file_exists
-    from huggingface_hub.errors import HfHubHTTPError
-
-    try:
-        return file_exists(pretrained_path, adapter_config_name, revision=revision)
-    except (HfHubHTTPError, OSError):
-        return False
-
-
 def make_policy(
     cfg: PreTrainedConfig,
     ds_meta: LeRobotDatasetMetadata | None = None,
@@ -638,24 +607,13 @@ def make_policy(
             "the PEFT config parameters to be set. For training with PEFT, see `lerobot_train.py` on how to do that."
         )
 
-    # When `use_peft=True` and a checkpoint is given, the checkpoint can be one of two things:
-    # 1. A base model checkpoint (e.g., a pretrained policy) on which we want to start a fresh PEFT fine-tune.
-    # 2. A PEFT adapter checkpoint (e.g., a previously trained PEFT adapter)
-    # We distinguish between these two cases
-    load_existing_adapter = (
-        cfg.pretrained_path
-        and cfg.use_peft
-        and _has_peft_adapter_config(str(cfg.pretrained_path), cfg.pretrained_revision)
-    )
-
-    if cfg.pretrained_path and not load_existing_adapter:
+    if cfg.pretrained_path and not cfg.use_peft:
         # Load a pretrained policy and override the config if needed (for example, if there are inference-time
-        # hyperparameters that we want to vary). This also covers starting a fresh PEFT fine-tune on top of a
-        # base model: the base weights are loaded here and `wrap_with_peft` builds the adapter afterwards.
+        # hyperparameters that we want to vary).
         kwargs["pretrained_name_or_path"] = cfg.pretrained_path
         kwargs["revision"] = cfg.pretrained_revision
         policy = policy_cls.from_pretrained(**kwargs)
-    elif load_existing_adapter:
+    elif cfg.pretrained_path and cfg.use_peft:
         # Load a pretrained PEFT model on top of the policy. The pretrained path points to the folder/repo
         # of the adapter and the adapter's config contains the path to the base policy. So we need the
         # adapter config first, then load the correct policy and then apply PEFT.

src/lerobot/policies/pretrained.py

@@ -23,6 +23,8 @@ from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import TYPE_CHECKING, TypedDict, TypeVar, Unpack
 
+import packaging
+import safetensors
 from huggingface_hub import HfApi, ModelCard, ModelCardData, hf_hub_download, save_torch_state_dict
 from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
 from huggingface_hub.errors import HfHubHTTPError
@@ -32,7 +34,6 @@ from torch import Tensor, nn
 from lerobot.__version__ import __version__
 from lerobot.configs import PreTrainedConfig
 from lerobot.configs.train import TrainPipelineConfig
-from lerobot.utils.device_utils import resolve_safetensors_device
 from lerobot.utils.hub import HubMixin
 
 from .utils import log_model_loading_keys
@@ -220,10 +221,26 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
 
     @classmethod
     def _load_as_safetensor(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
-        missing_keys, unexpected_keys = load_model_as_safetensor(
-            model, model_file, strict=strict, device=resolve_safetensors_device(map_location)
-        )
+        # Create base kwargs
+        kwargs = {"strict": strict}
+
+        # Add device parameter for newer versions that support it
+        if packaging.version.parse(safetensors.__version__) >= packaging.version.parse("0.4.3"):
+            kwargs["device"] = map_location
+
+        # Load the model with appropriate kwargs
+        missing_keys, unexpected_keys = load_model_as_safetensor(model, model_file, **kwargs)
         log_model_loading_keys(missing_keys, unexpected_keys)
+
+        # For older versions, manually move to device if needed
+        if "device" not in kwargs and map_location != "cpu":
+            logging.warning(
+                "Loading model weights on other devices than 'cpu' is not supported natively in your version of safetensors."
+                " This means that the model is loaded on 'cpu' first and then copied to the device."
+                " This leads to a slower loading time."
+                " Please update safetensors to version 0.4.3 or above for improved performance."
+            )
+            model.to(map_location)
         return model
 
     @abc.abstractmethod

src/lerobot/rewards/pretrained.py

@@ -21,6 +21,8 @@ from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import TYPE_CHECKING, Any, TypeVar
 
+import packaging
+import safetensors
 from huggingface_hub import HfApi, ModelCard, ModelCardData, hf_hub_download
 from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
 from huggingface_hub.errors import HfHubHTTPError
@@ -28,7 +30,6 @@ from safetensors.torch import load_model as load_model_as_safetensor, save_model
 from torch import Tensor, nn
 
 from lerobot.configs.rewards import RewardModelConfig
-from lerobot.utils.device_utils import resolve_safetensors_device
 from lerobot.utils.hub import HubMixin
 
 if TYPE_CHECKING:
@@ -128,13 +129,29 @@ class PreTrainedRewardModel(nn.Module, HubMixin, abc.ABC):
 
     @classmethod
     def _load_as_safetensor(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
-        missing_keys, unexpected_keys = load_model_as_safetensor(
-            model, model_file, strict=strict, device=resolve_safetensors_device(map_location)
-        )
+        # Create base kwargs
+        kwargs = {"strict": strict}
+
+        # Add device parameter for newer versions that support it
+        if packaging.version.parse(safetensors.__version__) >= packaging.version.parse("0.4.3"):
+            kwargs["device"] = map_location
+
+        # Load the model with appropriate kwargs
+        missing_keys, unexpected_keys = load_model_as_safetensor(model, model_file, **kwargs)
         if missing_keys:
             logging.warning(f"Missing key(s) when loading model: {missing_keys}")
         if unexpected_keys:
             logging.warning(f"Unexpected key(s) when loading model: {unexpected_keys}")
+
+        # For older versions, manually move to device if needed
+        if "device" not in kwargs and map_location != "cpu":
+            logging.warning(
+                "Loading model weights on other devices than 'cpu' is not supported natively in your version of safetensors."
+                " This means that the model is loaded on 'cpu' first and then copied to the device."
+                " This leads to a slower loading time."
+                " Please update safetensors to version 0.4.3 or above for improved performance."
+            )
+            model.to(map_location)
         return model
 
     def get_optim_params(self):

src/lerobot/scripts/lerobot_annotate.py

@@ -28,12 +28,7 @@ For distributed runs, see ``examples/annotations/run_hf_job.py``.
 """
 
 import logging
-from contextlib import suppress
 from pathlib import Path
-from typing import TYPE_CHECKING
-
-from huggingface_hub import HfApi, snapshot_download
-from huggingface_hub.errors import RevisionNotFoundError
 
 from lerobot.annotations.steerable_pipeline.config import AnnotationPipelineConfig
 from lerobot.annotations.steerable_pipeline.executor import Executor
@@ -47,12 +42,6 @@ from lerobot.annotations.steerable_pipeline.validator import StagingValidator
 from lerobot.annotations.steerable_pipeline.vlm_client import make_vlm_client
 from lerobot.annotations.steerable_pipeline.writer import LanguageColumnsWriter
 from lerobot.configs import parser
-from lerobot.utils.import_utils import _datasets_available, require_package
-
-if TYPE_CHECKING or _datasets_available:
-    from lerobot.datasets.dataset_metadata import CODEBASE_VERSION
-    from lerobot.datasets.io_utils import load_info
-    from lerobot.datasets.utils import create_lerobot_dataset_card
 
 logger = logging.getLogger(__name__)
 
@@ -61,6 +50,8 @@ def _resolve_root(cfg: AnnotationPipelineConfig) -> Path:
     if cfg.root is not None:
         return Path(cfg.root)
     if cfg.repo_id is not None:
+        from huggingface_hub import snapshot_download
+
         return Path(snapshot_download(repo_id=cfg.repo_id, repo_type="dataset"))
     raise ValueError("Either --root or --repo_id must be provided.")
 
@@ -134,7 +125,7 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
 
     Pushes to ``cfg.new_repo_id`` when set, otherwise back to ``cfg.repo_id``.
     """
-    require_package("datasets", "dataset")
+    from huggingface_hub import HfApi  # noqa: PLC0415
 
     repo_id = cfg.new_repo_id or cfg.repo_id
     commit_message = cfg.push_commit_message or "Add steerable annotations (lerobot-annotate)"
@@ -152,26 +143,33 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
         repo_id=repo_id,
         repo_type="dataset",
         commit_message=commit_message,
-        # README.md is excluded because when pushing to ``new_repo_id`` the
-        # source card's links (e.g. the visualize badge) would keep pointing
-        # at the source dataset; a fresh card is generated below instead.
-        ignore_patterns=[".annotate_staging/**", "**/.DS_Store", "README.md"],
+        ignore_patterns=[".annotate_staging/**", "**/.DS_Store"],
     )
     print(f"[lerobot-annotate] uploaded to https://huggingface.co/datasets/{repo_id}", flush=True)
 
-    dataset_info = load_info(root)
-    card = create_lerobot_dataset_card(dataset_info=dataset_info, license="apache-2.0", repo_id=repo_id)
-    card.push_to_hub(repo_id=repo_id, repo_type="dataset")
-
     # Tag the upload with the codebase version. ``LeRobotDatasetMetadata``
     # resolves the dataset revision via ``get_safe_version`` which scans
     # for tags like ``v3.0``; without a tag it raises
     # ``RevisionNotFoundError``. Read the version straight from the
     # dataset's own ``meta/info.json`` so we tag whatever the writer
     # actually wrote (no accidental drift if the codebase floor moves).
-    version_tag = (
-        dataset_info.codebase_version if dataset_info.codebase_version.startswith("v") else CODEBASE_VERSION
-    )
+    from lerobot.datasets.dataset_metadata import CODEBASE_VERSION  # noqa: PLC0415
+
+    info_path = root / "meta" / "info.json"
+    version_tag = CODEBASE_VERSION
+    if info_path.exists():
+        try:
+            from lerobot.utils.io_utils import load_json  # noqa: PLC0415
+
+            info = load_json(info_path)
+            ds_version = info.get("codebase_version")
+            if isinstance(ds_version, str) and ds_version.startswith("v"):
+                version_tag = ds_version
+        except Exception as exc:  # noqa: BLE001
+            print(
+                f"[lerobot-annotate] could not read codebase_version from info.json ({exc}); falling back to {version_tag}",
+                flush=True,
+            )
     revision = getattr(commit_info, "oid", None)
     tag_kwargs = {
         "repo_id": repo_id,
@@ -182,6 +180,10 @@ def _push_to_hub(root: Path, cfg: AnnotationPipelineConfig) -> None:
         tag_kwargs["revision"] = revision
 
     try:
+        from contextlib import suppress  # noqa: PLC0415
+
+        from huggingface_hub.errors import RevisionNotFoundError  # noqa: PLC0415
+
         with suppress(RevisionNotFoundError):
             api.delete_tag(repo_id, tag=version_tag, repo_type="dataset")
         api.create_tag(**tag_kwargs)

src/lerobot/scripts/lerobot_train.py

@@ -171,9 +171,6 @@ def update_policy(
     train_metrics.update_s = time.perf_counter() - start_time
     if torch.cuda.is_available():
         train_metrics.gpu_mem_gb = torch.cuda.max_memory_allocated() / (1024**3)
-    # Aggregate the policy's scalar outputs for logging and rank-reduction across the log window.
-    if output_dict:
-        train_metrics.update_metrics(output_dict)
     return train_metrics, output_dict
 
 
@@ -575,7 +572,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         batch = preprocessor(batch)
         train_tracker.dataloading_s = time.perf_counter() - start_time
 
-        train_tracker, _ = update_policy(
+        train_tracker, output_dict = update_policy(
             train_tracker,
             policy,
             batch,
@@ -608,10 +605,9 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
                     train_tracker.samples_per_s = effective_batch_size / step_time
                 logging.info(train_tracker)
                 if wandb_logger:
-                    # Policy sub-losses (latent_loss, action_loss, ...) are aggregated into the
-                    # tracker by update_policy, so to_dict() already carries their windowed,
-                    # rank-reduced averages — no per-step output_dict passthrough needed.
                     wandb_log_dict = train_tracker.to_dict()
+                    if output_dict:
+                        wandb_log_dict.update(output_dict)
                     # Log sample weighting statistics if enabled
                     if sample_weighter is not None:
                         weighter_stats = sample_weighter.get_stats()

src/lerobot/utils/device_utils.py

@@ -59,20 +59,6 @@ def get_safe_torch_device(try_device: str, log: bool = False) -> torch.device:
     return device
 
 
-def resolve_safetensors_device(map_location: str | torch.device) -> str:
-    """Resolve a device string for a safetensors load, working around a device-mapping quirk.
-
-    safetensors' load maps the bare string "cuda" to cuda:0 regardless of the current device
-    (unlike torch's .to("cuda"), which honors torch.cuda.current_device()). Under multi-GPU
-    accelerate/FSDP every rank would then load its weights onto GPU 0, OOMing it before sharding.
-    Resolve "cuda" to the concrete current-device index so each rank loads onto its own GPU.
-    """
-    map_location = str(map_location)
-    if map_location == "cuda" and torch.cuda.is_available():
-        return f"cuda:{torch.cuda.current_device()}"
-    return map_location
-
-
 def get_safe_dtype(dtype: torch.dtype, device: str | torch.device):
     """
     mps is currently not compatible with float64

src/lerobot/utils/logging_utils.py

@@ -104,7 +104,6 @@ class MetricsTracker:
         "episodes",
         "epochs",
         "accelerator",
-        "_caller_metrics",
     ]
 
     def __init__(
@@ -130,9 +129,6 @@ class MetricsTracker:
         self.episodes = self.samples / self._avg_samples_per_ep
         self.epochs = self.samples / self._num_frames
         self.accelerator = accelerator
-        # Meter names the caller registered up front. update_metrics() leaves these untouched, so a
-        # policy that echoes e.g. "loss" in its output dict can't clobber the aggregated meter.
-        self._caller_metrics: set[str] = set(self.metrics)
 
     def __getattr__(self, name: str) -> int | dict[str, AverageMeter] | AverageMeter | Any:
         if name in self.__dict__:
@@ -160,21 +156,6 @@ class MetricsTracker:
         self.episodes = self.samples / self._avg_samples_per_ep
         self.epochs = self.samples / self._num_frames
 
-    def update_metrics(self, values: dict[str, Any]) -> None:
-        """Accumulate a dict of scalar metrics, auto-registering a meter for each new key.
-
-        Non-numeric values and bools are ignored.
-        Caller-registered metrics (those passed to the constructor) are never overridden.
-        """
-        for name, value in values.items():
-            if isinstance(value, bool) or not isinstance(value, (int, float)):
-                continue
-            if name in self._caller_metrics:
-                continue
-            if name not in self.metrics:
-                self.metrics[name] = AverageMeter(name, ":.3f", reduction="mean")
-            self.metrics[name].update(float(value))
-
     def reduce_across_ranks(self) -> None:
         """
         Synchronises the running averages of every metric whose ``reduction`` is not ``"none"``

tests/annotations/test_modules.py

@@ -85,7 +85,7 @@ def _spy_responder(captured: list[list[dict[str, Any]]], reply: Any):
 def test_module1_plan_memory_subtask_smoke(fixture_dataset_root: Path, tmp_path: Path) -> None:
     vlm = make_canned_responder(
         {
-            "COMPLETED manipulation events": {
+            "atomic subtasks": {
                 "subtasks": [
                     {"text": "grasp the handle of the sponge", "start": 0.0, "end": 0.4},
                     {"text": "wipe the counter from left to right", "start": 0.4, "end": 0.8},
@@ -126,7 +126,7 @@ def test_module1_emit_memory_false_skips_memory_keeps_subtasks_and_plan(
     leaving subtask + plan generation intact — symmetric to ``emit_plan``."""
     vlm = make_canned_responder(
         {
-            "COMPLETED manipulation events": {
+            "atomic subtasks": {
                 "subtasks": [
                     {"text": "grasp the handle of the sponge", "start": 0.0, "end": 0.4},
                     {"text": "wipe the counter from left to right", "start": 0.4, "end": 0.8},
@@ -318,7 +318,7 @@ def test_module1_attaches_contact_sheets_to_subtask_prompt(
                     return block.get("text", "")
         return ""
 
-    subtask_calls = [m for m in captured if "COMPLETED manipulation events" in _prompt_text(m)]
+    subtask_calls = [m for m in captured if "atomic subtasks" in _prompt_text(m)]
     assert len(subtask_calls) == 1, "expected exactly one subtask-prompt VLM call"
     content = subtask_calls[0][0]["content"]
     video_blocks = [b for b in content if isinstance(b, dict) and b.get("type") == "video"]

tests/policies/test_peft_base_model_loading.py

@@ -1,142 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Regression tests for PEFT loading when the checkpoint is a base model (see issue #3975).
-
-Starting a fresh LoRA/PEFT fine-tune points ``--policy.path`` at a *base model* (no
-``adapter_config.json``) while also setting ``use_peft=True``. This must NOT be mistaken for
-loading an existing PEFT adapter. These tests lock in the base-model vs. adapter distinction
-made by ``lerobot.policies.factory._has_peft_adapter_config`` and the branch it drives in
-``make_policy``. They are pure/fast (no network, no ``peft``), so they run in CI.
-"""
-
-import json
-from unittest.mock import MagicMock, patch
-
-import torch
-from huggingface_hub.errors import HfHubHTTPError
-
-from lerobot.policies.factory import _has_peft_adapter_config
-
-
-def test_local_base_model_dir_has_no_adapter_config(tmp_path):
-    # A base-model checkpoint directory (only model weights, no adapter config).
-    (tmp_path / "model.safetensors").write_bytes(b"")
-    (tmp_path / "config.json").write_text("{}")
-    assert _has_peft_adapter_config(str(tmp_path)) is False
-
-
-def test_local_adapter_dir_has_adapter_config(tmp_path):
-    (tmp_path / "adapter_config.json").write_text(json.dumps({"peft_type": "LORA"}))
-    (tmp_path / "adapter_model.safetensors").write_bytes(b"")
-    assert _has_peft_adapter_config(str(tmp_path)) is True
-
-
-def test_hub_base_model_repo_has_no_adapter_config():
-    with patch("huggingface_hub.file_exists", return_value=False) as mock_exists:
-        assert _has_peft_adapter_config("lerobot/lingbot_va_base") is False
-    mock_exists.assert_called_once()
-    assert mock_exists.call_args.args[1] == "adapter_config.json"
-
-
-def test_hub_adapter_repo_has_adapter_config():
-    with patch("huggingface_hub.file_exists", return_value=True):
-        assert _has_peft_adapter_config("some/adapter-repo", revision="main") is True
-
-
-def test_hub_lookup_error_falls_back_to_base_model():
-    # Offline / private / transient Hub errors must not crash; treat as "not an adapter".
-    with patch(
-        "huggingface_hub.file_exists",
-        side_effect=HfHubHTTPError("boom", response=MagicMock()),
-    ):
-        assert _has_peft_adapter_config("some/private-repo") is False
-    with patch("huggingface_hub.file_exists", side_effect=OSError("offline")):
-        assert _has_peft_adapter_config("some/repo") is False
-
-
-def _make_dummy_policy_cfg(pretrained_path, use_peft):
-    cfg = MagicMock()
-    cfg.type = "act"
-    cfg.device = "cpu"
-    cfg.pretrained_path = pretrained_path
-    cfg.pretrained_revision = None
-    cfg.use_peft = use_peft
-    cfg.input_features = {}
-    cfg.output_features = {}
-    return cfg
-
-
-@patch("lerobot.policies.factory.validate_visual_features_consistency")
-@patch("lerobot.policies.factory.env_to_policy_features", return_value={})
-@patch("lerobot.policies.factory.get_policy_class")
-def test_make_policy_base_model_with_use_peft_loads_base_not_adapter(
-    mock_get_cls, _mock_features, _mock_validate
-):
-    """`use_peft=True` on a base model must load the base weights, not a PEFT adapter.
-
-    Before the #3975 fix this went down the ``PeftConfig.from_pretrained`` path and failed
-    looking for a non-existent ``adapter_config.json``.
-    """
-    from lerobot.policies import factory
-
-    policy_cls = MagicMock()
-    loaded_policy = torch.nn.Linear(1, 1)  # a real nn.Module so make_policy's assert passes
-    policy_cls.from_pretrained.return_value = loaded_policy
-    mock_get_cls.return_value = policy_cls
-
-    cfg = _make_dummy_policy_cfg(pretrained_path="lerobot/lingbot_va_base", use_peft=True)
-    env_cfg = MagicMock()
-
-    with patch.object(factory, "_has_peft_adapter_config", return_value=False) as mock_has_adapter:
-        policy = factory.make_policy(cfg=cfg, env_cfg=env_cfg)
-
-    mock_has_adapter.assert_called_once()
-    # Base model is loaded via the normal pretrained path...
-    policy_cls.from_pretrained.assert_called_once()
-    assert policy_cls.from_pretrained.call_args.kwargs["pretrained_name_or_path"] == (
-        "lerobot/lingbot_va_base"
-    )
-    # ...and PEFT adapter loading is NOT attempted (would need peft + adapter_config.json).
-    assert policy is loaded_policy
-
-
-@patch("lerobot.policies.factory.validate_visual_features_consistency")
-@patch("lerobot.policies.factory.env_to_policy_features", return_value={})
-@patch("lerobot.policies.factory.get_policy_class")
-def test_make_policy_existing_adapter_uses_peft_loading(mock_get_cls, _mock_features, _mock_validate):
-    """A real adapter checkpoint (has ``adapter_config.json``) must go through PEFT loading."""
-    from lerobot.policies import factory
-
-    policy_cls = MagicMock()
-    mock_get_cls.return_value = policy_cls
-
-    cfg = _make_dummy_policy_cfg(pretrained_path="some/adapter-repo", use_peft=True)
-    env_cfg = MagicMock()
-
-    policy_cls.from_pretrained.return_value = torch.nn.Linear(1, 1)
-
-    fake_peft = MagicMock()
-    fake_peft_config = MagicMock()
-    fake_peft_config.base_model_name_or_path = "lerobot/lingbot_va_base"
-    fake_peft.PeftConfig.from_pretrained.return_value = fake_peft_config
-    fake_peft.PeftModel.from_pretrained.return_value = torch.nn.Linear(1, 1)
-
-    with (
-        patch.object(factory, "_has_peft_adapter_config", return_value=True),
-        patch.dict("sys.modules", {"peft": fake_peft}),
-    ):
-        factory.make_policy(cfg=cfg, env_cfg=env_cfg)
-
-    fake_peft.PeftConfig.from_pretrained.assert_called_once_with("some/adapter-repo")
-    fake_peft.PeftModel.from_pretrained.assert_called_once()

tests/scripts/test_lerobot_annotate.py

@@ -18,8 +18,6 @@ import json
 from types import SimpleNamespace
 
 import pytest
-import requests
-from huggingface_hub.errors import RevisionNotFoundError
 
 # ``lerobot.scripts.lerobot_annotate`` (and the ``_push_to_hub`` path it
 # exercises) imports ``lerobot.datasets``, which only ships under the
@@ -28,13 +26,11 @@ pytest.importorskip("datasets", reason="datasets is required (install lerobot[da
 
 
 def test_push_to_hub_tags_uploaded_dataset_revision(tmp_path, monkeypatch):
-    from lerobot.scripts import lerobot_annotate
+    from lerobot.scripts.lerobot_annotate import _push_to_hub
 
     root = tmp_path / "dataset"
     (root / "meta").mkdir(parents=True)
-    (root / "meta" / "info.json").write_text(
-        json.dumps({"codebase_version": "v3.0", "fps": 30, "features": {}})
-    )
+    (root / "meta" / "info.json").write_text(json.dumps({"codebase_version": "v3.0"}))
 
     calls = {}
 
@@ -47,6 +43,9 @@ def test_push_to_hub_tags_uploaded_dataset_revision(tmp_path, monkeypatch):
             return SimpleNamespace(oid="abc123")
 
         def delete_tag(self, repo_id, **kwargs):
+            import requests
+            from huggingface_hub.errors import RevisionNotFoundError
+
             calls["delete_tag"] = {"repo_id": repo_id, **kwargs}
             # Simulate the common case: no stale tag to delete.
             raise RevisionNotFoundError("no such tag", response=requests.Response())
@@ -54,12 +53,7 @@ def test_push_to_hub_tags_uploaded_dataset_revision(tmp_path, monkeypatch):
         def create_tag(self, **kwargs):
             calls["create_tag"] = kwargs
 
-    monkeypatch.setattr(lerobot_annotate, "HfApi", FakeHfApi)
-
-    def fake_card_push(self, **kwargs):
-        calls["card_push"] = {"content": str(self), **kwargs}
-
-    monkeypatch.setattr("huggingface_hub.DatasetCard.push_to_hub", fake_card_push)
+    monkeypatch.setattr("huggingface_hub.HfApi", FakeHfApi)
 
     cfg = SimpleNamespace(
         repo_id="source/dataset",
@@ -68,7 +62,7 @@ def test_push_to_hub_tags_uploaded_dataset_revision(tmp_path, monkeypatch):
         push_commit_message=None,
     )
 
-    lerobot_annotate._push_to_hub(root, cfg)
+    _push_to_hub(root, cfg)
 
     assert calls["create_repo"] == {
         "repo_id": "annotated/dataset",
@@ -77,13 +71,6 @@ def test_push_to_hub_tags_uploaded_dataset_revision(tmp_path, monkeypatch):
         "exist_ok": True,
     }
     assert calls["upload_folder"]["repo_id"] == "annotated/dataset"
-    # The source README must not be copied over: its links (e.g. the
-    # visualize badge) point at the source dataset. A card regenerated for
-    # the target repo is pushed instead.
-    assert "README.md" in calls["upload_folder"]["ignore_patterns"]
-    assert calls["card_push"]["repo_id"] == "annotated/dataset"
-    assert "visualize_dataset?path=annotated/dataset" in calls["card_push"]["content"]
-    assert "source/dataset" not in calls["card_push"]["content"]
     # A stale tag (e.g. from a previous annotation run) is deleted first so
     # the new tag always points at the upload we just made.
     assert calls["delete_tag"] == {

tests/utils/test_logging_utils.py

@@ -233,37 +233,3 @@ def test_metrics_tracker_reduce_across_ranks_invokes_reduce():
     # accumulate against the cluster view rather than the stale per-rank sum.
     meter = tracker.update_s
     assert meter.sum / meter.count == pytest.approx(meter.avg)
-
-
-def test_metrics_tracker_update_metrics_registers_and_averages():
-    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics={})
-    tracker.update_metrics({"latent_loss": 0.2, "action_loss": 0.4})
-    tracker.update_metrics({"latent_loss": 0.4, "action_loss": 0.6})
-
-    # New keys are auto-registered as mean-reduced meters and averaged over the window.
-    assert tracker.metrics["latent_loss"].reduction == "mean"
-    assert tracker.metrics["latent_loss"].avg == pytest.approx(0.3)
-    assert tracker.metrics["action_loss"].avg == pytest.approx(0.5)
-    assert tracker.to_dict()["latent_loss"] == pytest.approx(0.3)
-
-
-def test_metrics_tracker_update_metrics_skips_non_numeric():
-    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics={})
-    tracker.update_metrics({"loss": 0.5, "head_mode": "sparse", "enabled": True})
-
-    # strings and bools ignored
-    assert "loss" in tracker.metrics
-    assert "head_mode" not in tracker.metrics
-    assert "enabled" not in tracker.metrics
-
-
-def test_metrics_tracker_update_metrics_does_not_override_caller_meter():
-    # A policy that echoes "loss" in its output dict must not overwrite the caller-owned,
-    # already-aggregated loss meter.
-    metrics = {"loss": AverageMeter("loss", ":.3f", reduction="mean")}
-    tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=metrics)
-    tracker.loss = 1.0  # caller-set optimized loss
-    tracker.update_metrics({"loss": 99.0, "latent_loss": 0.2})
-
-    assert tracker.metrics["loss"].avg == pytest.approx(1.0)  # snapshot ignored
-    assert tracker.metrics["latent_loss"].avg == pytest.approx(0.2)