How do I install the Weight Conversion skill?

Run npx skillmds@latest add leondgarse/weight-conversion in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Weight Conversion free to use?

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Who published Weight Conversion?

leondgarse (@leondgarse) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Weight Conversion by leondgarse — Agent Skill

Name: Weight Conversion
Author: leondgarse

File contents

Weight Conversion Skill

Convert pretrained PyTorch weights to Keras h5 format.

The core task is aligning the weight name order between torch and keras models. download_and_load.keras_reload_from_torch_model is a convenience helper that automates this, but direct manual conversion is also fine.

Pipeline Overview

Weight collection (state_dict_stack_by_layer): Groups torch state_dict entries by layer name (splitting on .), filtering via skip_weights and unstack_weights.
Name alignment (align_layer_names_multi_stage): Reorders keras layer names to match torch weight order.
Weight transfer (keras_reload_stacked_state_dict): Applies standard transforms (Conv2D/Dense transpose, etc.) plus custom additional_transfer overrides, then saves.

Parameter Reference

Weight Collection

Parameter	Purpose
`skip_weights`	Weight name suffixes to drop (e.g., `["num_batches_tracked", "relative_position_index"]`)
`unstack_weights`	Weights kept as individual entries instead of grouped with their layer (e.g., `["cls_token", "pos_embed", "gamma_1"]`)

Name Alignment (order matching)

Parameter	Purpose
`tail_align_dict`	Reposition layers by tail name: `{tail_name: offset}`. Negative offset moves layer earlier. Can be scoped by stack: `{"stack3": {"attn_gamma": -6}}`
`full_name_align_dict`	Reposition by exact name: value can be negative offset, absolute position, or another layer's name string
`tail_split_position`	Where to split name into head/tail (default `2`). E.g., `1` → head=`stack1`, tail=`attn_gamma`
`specific_match_func`	Function returning the complete ordered name list, bypassing all alignment logic. Use for complex cases where dicts can't express the mapping

Weight Transfer

Parameter	Purpose
`additional_transfer`	Custom transforms: `{LayerClass: lambda ww: [...]}` or `{"name_suffix": lambda ww: [...]}`. Applied after default Conv2D/Dense transposes

Workflow

Create keras model with pretrained=None, classifier_activation=None
Run with do_convert=False first to inspect both name lists
Compare printed torch/keras weight lists — find misalignments
Configure alignment parameters to fix ordering
Run with do_convert=True — it predicts with both models and prints results
Verify top prediction matches (usually Egyptian_cat for the cat test image)
md5sum output.h5 → add hash to PRETRAINED_DICT
Upload to GitHub releases. Notify user if cannot upload directly.

Troubleshooting

Shape mismatch: Dense/Conv transposes are automatic; check if combined QKV needs unstack_weights
Name ordering wrong: Use do_convert=False to see lists side-by-side; adjust offsets or use specific_match_func for full control
Predictions don't match: Check rescale_mode in add_pre_post_process(), classifier_activation, or intermediate layer outputs

---name: weight-conversiondescription: Converting PyTorch model weights to Keras h5 format for keras_cv_attention_models---# Weight Conversion SkillConvert pretrained PyTorch weights to Keras h5 format.The core task is **aligning the weight name order** between torch and keras models. `download_and_load.keras_reload_from_torch_model` is a convenience helper that automates this, but direct manual conversion is also fine.## Pipeline Overview1. **Weight collection** (`state_dict_stack_by_layer`): Groups torch `state_dict` entries by layer name (splitting on `.`), filtering via `skip_weights` and `unstack_weights`.2. **Name alignment** (`align_layer_names_multi_stage`): Reorders keras layer names to match torch weight order.3. **Weight transfer** (`keras_reload_stacked_state_dict`): Applies standard transforms (Conv2D/Dense transpose, etc.) plus custom `additional_transfer` overrides, then saves.## Parameter Reference### Weight Collection| Parameter | Purpose ||-----------|---------|| `skip_weights` | Weight name suffixes to drop (e.g., `["num_batches_tracked", "relative_position_index"]`) || `unstack_weights` | Weights kept as individual entries instead of grouped with their layer (e.g., `["cls_token", "pos_embed", "gamma_1"]`) |### Name Alignment (order matching)| Parameter | Purpose ||-----------|---------|| `tail_align_dict` | Reposition layers by tail name: `{tail_name: offset}`. Negative offset moves layer earlier. Can be scoped by stack: `{"stack3": {"attn_gamma": -6}}` || `full_name_align_dict` | Reposition by exact name: value can be negative offset, absolute position, or another layer's name string || `tail_split_position` | Where to split name into head/tail (default `2`). E.g., `1` → head=`stack1`, tail=`attn_gamma` || `specific_match_func` | Function returning the complete ordered name list, bypassing all alignment logic. Use for complex cases where dicts can't express the mapping |### Weight Transfer| Parameter | Purpose ||-----------|---------|| `additional_transfer` | Custom transforms: `{LayerClass: lambda ww: [...]}` or `{"name_suffix": lambda ww: [...]}`. Applied after default Conv2D/Dense transposes |## Workflow1. Create keras model with `pretrained=None, classifier_activation=None`2. Run with `do_convert=False` first to inspect both name lists3. Compare printed torch/keras weight lists — find misalignments4. Configure alignment parameters to fix ordering5. Run with `do_convert=True` — it predicts with both models and prints results6. Verify top prediction matches (usually `Egyptian_cat` for the cat test image)7. `md5sum output.h5` → add hash to `PRETRAINED_DICT`8. Upload to GitHub releases. Notify user if cannot upload directly.## Troubleshooting- **Shape mismatch**: Dense/Conv transposes are automatic; check if combined QKV needs `unstack_weights`- **Name ordering wrong**: Use `do_convert=False` to see lists side-by-side; adjust offsets or use `specific_match_func` for full control- **Predictions don't match**: Check `rescale_mode` in `add_pre_post_process()`, `classifier_activation`, or intermediate layer outputs

Frequently asked questions

Weight Conversion