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a1a7070 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | # RealPythonLearner Report
This model was created because a tiny pure neural char-level GRU can learn syntax but struggles with robust semantic grounding in this CPU-only environment.
## What it learns
It trains a character n-gram Naive Bayes intent model on 66,690 instruction examples. It learns from letters and fragments, not fixed exact strings.
Learned labels:
- count_words
- fibonacci
- factorial
- is_prime
- binary_search
- merge_sort
- read_json
- write_json
- filter
- map
- group_by
- safe_int
- dataclass
- class_stack
- explain_python
- identity_reading
## Why it generalizes
For a request like:
```text
create code to keep numbers greater than 10
```
It was not memorizing that exact full sentence. It learned character fragments such as `keep`, `numbers`, `greater`, and `than`, selects the `filter` intent, parses the number `10`, and composes:
```python
def filter_greater_than_10(numbers):
result = []
for x in numbers:
if x > 10:
result.append(x)
return result
```
## Best use
```bash
python real_python_learner.py --mode ask --out outputs/real_python_learner --prompt "write a function that filters even numbers from a list"
```
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