docs: showcase advanced training parameters in README#1
docs: showcase advanced training parameters in README#1AndresNinou wants to merge 1 commit intomainfrom
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Adds an "Advanced training" section between "Run" and "Expected output" demonstrating the 8 new optional kwargs on `client.training.lora_finetune(...)`: lora_alpha, lora_dropout, target_modules, warmup_steps, gradient_checkpointing, freeze_vision_encoder, dtype, save_freq. Includes a single end-to-end example wiring up all 8 with production-grade defaults, plus a bounds table and the pi0.5 target_modules whitelist. Notes that LoRA-only kwargs are rejected on full fine-tunes. Requires reflex-sdk >= 0.2.0.
📝 WalkthroughWalkthroughThis PR adds an "Advanced training" section to the README documenting optional LoRA finetune kwargs supported by ChangesDocumentation
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Actionable comments posted: 3
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@README.md`:
- Around line 155-164: Clarify the README's `lora_alpha` default by replacing
"derived from `lora_rank`" with the explicit derivation formula or an example;
update the table row for `lora_alpha` to state the exact rule (e.g., "defaults
to 2 * lora_rank" or "defaults to lora_rank — typically 2 * lora_rank") and, if
applicable, add a short parenthetical example to show numeric behavior when
`lora_rank` is X; edit the README entry for `lora_alpha` (and nearby `lora_rank`
docs if present) so users see the concrete default computation and an example.
- Line 152: The README example uses job["training_job_id"] which is inconsistent
with the documented identifier name run_id; update the example to access the
returned identifier using job["run_id"] (or the equivalent run_id key) so the
snippet matches other references to run_id throughout the docs and examples.
- Around line 128-153: The README example uses a non-matching API surface
(Reflex()/lots of params) — update it to match quickstart.py by replacing the
import/instantiation and call to reflect the real SDK: use
reflex.Client(api_key=...) instead of Reflex(), and call training.lora_finetune
with the actual supported parameters (hf_source_uri, model_name, base_model,
epochs) or, if you want to keep the extended knobs, explicitly document that
only reflex-sdk>=0.1.4 supports them and list which parameters are accepted;
ensure the README's example and parameter names (training.lora_finetune,
hf_source_uri, model_name, base_model, epochs, reflex.Client) exactly match the
implementation in quickstart.py.
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| ```python | ||
| from reflex import Reflex | ||
|
|
||
| client = Reflex() | ||
| job = client.training.lora_finetune( | ||
| hf_source_uri="hf://lerobot/aloha_sim_transfer_cube_human", | ||
| model_name="pi05-aloha-cube", | ||
| model_version="v1", | ||
| # Core knobs: | ||
| max_steps=2000, | ||
| batch_size=8, | ||
| learning_rate=1e-4, | ||
| lora_rank=16, | ||
| # Advanced — all optional, all bounds-checked server-side: | ||
| lora_alpha=32, # [1, 256] | ||
| lora_dropout=0.05, # [0.0, 0.5] | ||
| target_modules=["q_proj", "k_proj", "v_proj", "o_proj"], | ||
| warmup_steps=200, # [0, max_steps/2] | ||
| gradient_checkpointing=True, # save VRAM | ||
| freeze_vision_encoder=True, # standard for LoRA | ||
| dtype="bfloat16", # {"bfloat16", "float32"} | ||
| save_freq=500, # [50, max_steps] | ||
| ) | ||
|
|
||
| print(job["training_job_id"]) | ||
| ``` |
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🧩 Analysis chain
🌐 Web query:
What is the latest published version of reflex-sdk on PyPI?
💡 Result:
The latest published version of reflex-sdk on PyPI is v0.1.4.[1]
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Update the README code example to match the actual SDK in quickstart.py.
The documented API in README.md (lines 128–153) does not match the working implementation in quickstart.py (lines 154–159). The README shows parameters like model_version, max_steps, batch_size, lora_rank, lora_alpha, lora_dropout, target_modules, warmup_steps, gradient_checkpointing, freeze_vision_encoder, dtype, and save_freq, plus a different import and instantiation pattern (from reflex import Reflex / Reflex()), but the actual code uses only hf_source_uri, model_name, base_model, and epochs with reflex.Client(api_key=...). Developers following the README will encounter errors. Replace the README example with the actual working call pattern from quickstart.py, or document which parameters are genuinely supported by reflex-sdk>=0.1.4.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@README.md` around lines 128 - 153, The README example uses a non-matching API
surface (Reflex()/lots of params) — update it to match quickstart.py by
replacing the import/instantiation and call to reflect the real SDK: use
reflex.Client(api_key=...) instead of Reflex(), and call training.lora_finetune
with the actual supported parameters (hf_source_uri, model_name, base_model,
epochs) or, if you want to keep the extended knobs, explicitly document that
only reflex-sdk>=0.1.4 supports them and list which parameters are accepted;
ensure the README's example and parameter names (training.lora_finetune,
hf_source_uri, model_name, base_model, epochs, reflex.Client) exactly match the
implementation in quickstart.py.
| save_freq=500, # [50, max_steps] | ||
| ) | ||
|
|
||
| print(job["training_job_id"]) |
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# Description: Check for identifier naming patterns in the README
rg -n 'run_id|training_job_id' README.mdRepository: reflex-inc/quickstart
Length of output: 653
Fix inconsistent key name in code example.
Line 152 accesses job["training_job_id"], but the documentation consistently references run_id as the returned identifier (lines 26, 192, 196, 246, 249). Update the code example to use the correct key name for consistency.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@README.md` at line 152, The README example uses job["training_job_id"] which
is inconsistent with the documented identifier name run_id; update the example
to access the returned identifier using job["run_id"] (or the equivalent run_id
key) so the snippet matches other references to run_id throughout the docs and
examples.
| | Kwarg | Default if omitted | Bounds | What it does | | ||
| |---|---|---|---| | ||
| | `lora_alpha` | derived from `lora_rank` | [1, 256] | LoRA scaling factor | | ||
| | `lora_dropout` | `0.0` | [0.0, 0.5] | Dropout on LoRA layers | | ||
| | `target_modules` | full pi0.5 set | whitelist below | Modules to LoRA-adapt | | ||
| | `warmup_steps` | `100` | [0, `max_steps/2`] | LR warmup length | | ||
| | `gradient_checkpointing` | `False` | bool | Trade compute for VRAM | | ||
| | `freeze_vision_encoder` | `True` | bool | Freeze the vision tower | | ||
| | `dtype` | `"bfloat16"` | `{"bfloat16", "float32"}` | Compute dtype | | ||
| | `save_freq` | `500` | [50, `max_steps`] | Steps between checkpoints | |
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🧹 Nitpick | 🔵 Trivial | ⚡ Quick win
Consider clarifying the "derived from lora_rank" default.
The table states that lora_alpha defaults to a value "derived from lora_rank" when omitted. For users to understand the actual default behavior, consider documenting the derivation formula or providing an example (e.g., "typically 2 * lora_rank" or "defaults to lora_rank").
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@README.md` around lines 155 - 164, Clarify the README's `lora_alpha` default
by replacing "derived from `lora_rank`" with the explicit derivation formula or
an example; update the table row for `lora_alpha` to state the exact rule (e.g.,
"defaults to 2 * lora_rank" or "defaults to lora_rank — typically 2 *
lora_rank") and, if applicable, add a short parenthetical example to show
numeric behavior when `lora_rank` is X; edit the README entry for `lora_alpha`
(and nearby `lora_rank` docs if present) so users see the concrete default
computation and an example.
1 participant
Summary
Adds an Advanced training section between Run and Expected output demonstrating the 8 new optional kwargs on `client.training.lora_finetune(...)` shipped in reflex-sdk 0.2.0:
Includes a single self-contained example with all 8 wired up to production-grade defaults, plus a bounds table so users know exactly what the server will accept. Calls out that LoRA-only kwargs are rejected on full fine-tunes.
Requires `reflex-sdk >= 0.2.0`.
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