pytorch-loss-implementation SKILL.md skillA
pytorch-loss-implementation from cxcscmu/skilllearnbench is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add cxcscmu/skilllearnbench/pytorch-loss-implementation@git:20260424.d50cf5aPins git:20260424.d50cf5a in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- cxcscmu/skilllearnbench
- Path
skills/b1-one-shot-claude-haiku-4-5/nlp-paper-reproduction/pytorch-loss-implementation/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
- Size
- 2,294 bytes
- Repository stars
- 91
- License
- MIT
Implement loss functions in PyTorch with proper tensor operations.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# PyTorch Loss Implementation
## Key Concepts
### 1. Tensor Operations
```python
import torch
# Sigmoid function
sigmoid_output = torch.sigmoid(input_tensor)
# Log function
log_output = torch.log(input_tensor)
# Mean reduction
mean_loss = loss.mean()
# Sum reduction
sum_loss = loss.sum()
```
### 2. Batch Processing
```python
# Batch dimension handling
batch_size = tensor.shape[0]
x = tensor[:batch_size//2] # First half
y = tensor[batch_size//2:] # Second half
# Ensure same device and dtype
tensor = tensor.to(device=model.device, dtype=torch.float32)
```
### 3. Numerical Stability
**Log-Sigmoid Stability**
```python
# Avoid: log(sigmoid(x)) can cause numerical issues
# Instead use:
stable_loss = torch.nn.functional.logsigmoid(x)
# Or manually:
loss = -torch.log(torch.sigmoid(x) + 1e-10)
```
**Handling Small Values**
```python
# Add epsilon to avoid log(0)
safe_log = torch.log(value + 1e-8)
# Clamp to valid range
clamped = torch.clamp(value, min=1e-10, max=1.0)
```
### 4. Device Handling
```python
# Ensure all tensors on same device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tensor = tensor.to(device)
# Or get from model
…Read the whole file at its exact version.
How to install
mdr add cxcscmu/skilllearnbench/pytorch-loss-implementation@sha256:d46f02386a7c6c0eThe line at the top of this page pins the label git:20260424.d50cf5a, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_dsxbbo76tea4af6f)
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Versions
The registry has recorded 1 version of this file, committed on Apr 24, 2026.
Audit of the latest version
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Source
cxcscmu/skilllearnbench · 91 stars · license MIT · pushed 2026-10-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_dsxbbo76tea4af6f GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/pytorch-loss-implementation GET https://markdownregistry.com/api/v1/blob/d46f02386a7c6c0ed45b193197b16e1194e0b71612d17526e82d7cbb25b08dd9
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.