mhc
- Repo stars 39
- Author repo awesome-omni-skill
mHC Skill
Manifold-Constrained Hyper-Connections (mHC) uses Doubly Stochastic Matrices to improve Deep Learning stability.
Contents
- Examples
- Full JAX implementation of
sinkhorn_knoppandmhc_layer_forward.
- Full JAX implementation of
- Deep Theory
- Motivation, stability proofs, and scalability arguments.
Usage
Use this skill when implementing Deep Transformers (1000+ layers) where standard residual connections fail (Gradient Vanishing, Representation Collapse).
# Quick Ref: Sinkhorn-Knopp (See examples.md for full context)
def sinkhorn_knopp(log_matrix, n_iters=20):
M = jnp.exp(log_matrix)
def body(i, m):
m /= m.sum(axis=1, keepdims=True)
m /= m.sum(axis=0, keepdims=True)
return m
return jax.lax.fori_loop(0, n_iters, body, M)- Fluxly category
- AI
- Author-declared agents
- No explicit declaration found; this is not inferred or tested compatibility
- Static check
- 88 / 100 · heuristic scan, not runtime safety proof
- Author / version / license
- @diegosouzapw · no license declared
- Fluxly token estimate
- Lean
- Fluxly setup estimate
- Plug-and-play
- External API key
- No requirement detected
- Detected OS requirements
- Unspecified
- Runtime requirements
- Python
- Detected file/system behavior
-
- Read-only
- Detected network behavior
- Local-only
- Install commands
- None (reference only)
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
The current SKILL.md does not define a fixed output example. Examples Full JAX implementation of sinkhornknopp and mhclayerforward. Deep Theory
Use this skill when implementing Deep Transformers (1000+ layers) where standard residual connections fail (Gradient Vanishing, Representation Collapse).
# mHC Skill
Manifold-Constrained Hyper-Connections (mHC) uses Doubly Stochastic Matrices to improve Deep Learning stability.
## Contents
- [Examples](examples.md)
- Full JAX implementation of `sinkhorn_knopp` and `mhc_layer_forward`.
- [Deep Theory](reference.md)
- Motivation, stability proofs, and scalability arguments.
## Usage
Use this skill when implementing Deep Transformers (1000+ layers) where standard residual connections fail (Gradient Vanishing, Representation Collapse).
```python
# Quick Ref: Sinkhorn-Knopp (See examples.md for full context)
def sinkhorn_knopp(log_matrix, n_iters=20):
M = jnp.exp(log_matrix)
def body(i, m):
m /= m.sum(axis=1, keepdims=True)
m /= m.sum(axis=0, keepdims=True)
return m
return jax.lax.fori_loop(0, n_iters, body, M)
``` Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.
sections -> Contents → Usage
terms -> Manifold-Constrained Hyper-Connections (mHC) uses Doubly Stochastic Matrices to improve Deep Learning stability. · - [Examples](examples.md) - Full JAX implementation of sinkhornknopp and mhclayerforward.
files/cmd -> sinkhornknopp · mhclayerforward · examples.md · reference.md
body sha256 -> 527c6edee267
Decide Fit First
Design Intent
How To Use It
Boundaries And Review