Implement tiny neural fallback model
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
No files or tests are named. Start from the proposed NeuralFallback.generate interface and the existing n-gram lookup path, then resolve the architecture and training approach. Done means a conditioned model, training and resumable checkpointing workflow, and CPU inference meeting the stated size and latency limits.
Written by the indexing model from the issue text.
Description
Summary
Implement a small neural language model to handle cases where n-gram lookup has low confidence.
Success Criteria
- Model architecture chosen (e.g., small transformer, LSTM)
- Model size < 50MB (target: runs on CPU efficiently)
- Inference time < 100ms on CPU
- Model can be conditioned on partial context
- Training script implemented
- Checkpointing and resumption supported
Architecture Candidates
- Tiny Transformer: 2-4 layers, 128-256 hidden, ~5M params
- LSTM: 2 layers, 256 hidden, ~3M params
- Distilled from larger model: Knowledge distillation approach
Constraints
- Must run locally on CPU
- Must fit in memory alongside n-gram tables
- Latency acceptable for interactive use
Interface
```python
class NeuralFallback:
def generate(self, context: list[str], max_tokens: int = 50) -> str:
"""Generate continuation given context."""
...
```
- Dominant language
- Python
- Stars
- 0
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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