231 lines
7.3 KiB
Markdown
231 lines
7.3 KiB
Markdown
---
|
||
license: apache-2.0
|
||
language:
|
||
- en
|
||
tags:
|
||
- code
|
||
- execution
|
||
- prediction
|
||
- language-generalization
|
||
- no-compiler
|
||
- python
|
||
- javascript
|
||
- lua
|
||
- cobol
|
||
- synthetic-languages
|
||
- transformers
|
||
- qwen2
|
||
pipeline_tag: text-generation
|
||
base_model: Qwen/Qwen2.5-1.5B
|
||
library_name: transformers
|
||
---
|
||
|
||
# CaaLM/CaaLM-v1
|
||
|
||
|
||
|
||
|
||
## What is this?
|
||
|
||
CaaLM (Code as a Language Model) is a 1.5B parameter model that predicts the output of code — without a compiler, runtime, or interpreter.
|
||
|
||
You give it code. It tells you what it would print.
|
||
|
||
The interesting part: it was never trained on a fixed set of languages. Instead, it was trained on real languages (Python, JavaScript, Lua, COBOL) alongside 200 synthetically generated fake programming languages — each with randomized syntax but consistent semantics. The goal was to teach the model what *execution* means, not what any specific language looks like.
|
||
|
||
This means it can predict the output of languages it has never seen before.
|
||
|
||
## Performance
|
||
|
||
|
||
|
||
|
||
|
||
**Overall: 96.2% (50/52 tests)**
|
||
|
||
| Category | Accuracy | Passed/Total |
|
||
|---|---|---|
|
||
| Real: Python | 100% | 10/10 |
|
||
| Real: JavaScript | 100% | 8/8 |
|
||
| Real: Lua | 100% | 6/6 |
|
||
| Real: COBOL | 75% | 3/4 |
|
||
| Novel Fake: Tier 1 (assign + print) | 100% | 8/8 |
|
||
| Novel Fake: Tier 2 (conditionals) | 86% | 6/7 |
|
||
| Novel Fake: Tier 3 (loops) | 100% | 4/4 |
|
||
| Edge Cases | 100% | 5/5 |
|
||
|
||
The novel fake language tests use languages that were never seen during training — completely invented syntax like `SCRIBBLE @x BECOMES 7` or `WONDER n > 10`. The model infers semantics from context and gets them right.
|
||
|
||
### Known Failures
|
||
|
||
Two failures in the benchmark, both explainable:
|
||
|
||
- **COBOL zero-padding** — predicted `08` instead of `0008`. Got the value right, missed the `PIC 9(4)` padding format. Data consistency issue.
|
||
- **If-without-else** — when a conditional has no else branch and the condition is false, the correct output is empty. The model predicted `NO`, hallucinating an else branch. Most training data had if/else pairs so it defaulted to that pattern.
|
||
|
||
## How It Works
|
||
|
||
Input format:
|
||
```
|
||
Code:
|
||
<your code here>
|
||
|
||
Output:
|
||
```
|
||
|
||
The model completes the `Output:` section with the predicted stdout.
|
||
|
||
### Example — Real Language
|
||
|
||
```
|
||
Code:
|
||
a = 10
|
||
b = 20
|
||
print(a + b)
|
||
|
||
Output:
|
||
30
|
||
```
|
||
|
||
### Example — Novel Fake Language (never seen during training)
|
||
|
||
```
|
||
Code:
|
||
SCRIBBLE @x BECOMES 7
|
||
SCRIBBLE @y BECOMES 3
|
||
YELL @x + @y
|
||
|
||
Output:
|
||
10
|
||
```
|
||
|
||
```
|
||
Code:
|
||
BIND n TO 15
|
||
WONDER n > 10
|
||
SHOUT YES
|
||
STOP
|
||
|
||
Output:
|
||
YES
|
||
```
|
||
|
||
## Quick Start
|
||
|
||
```python
|
||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||
import torch
|
||
|
||
model = AutoModelForCausalLM.from_pretrained(
|
||
"CaaLM/CaaLM-v1",
|
||
torch_dtype=torch.bfloat16,
|
||
device_map="auto"
|
||
)
|
||
tokenizer = AutoTokenizer.from_pretrained("CaaLM/CaaLM-v1")
|
||
model.eval()
|
||
|
||
def predict_output(code: str) -> str:
|
||
prompt = f"Code:\n{code}\n\nOutput:\n"
|
||
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
|
||
|
||
with torch.no_grad():
|
||
outputs = model.generate(
|
||
**inputs,
|
||
max_new_tokens=128,
|
||
do_sample=False,
|
||
pad_token_id=tokenizer.eos_token_id,
|
||
)
|
||
|
||
return tokenizer.decode(
|
||
outputs[0][inputs.input_ids.shape[1]:],
|
||
skip_special_tokens=True
|
||
).strip()
|
||
|
||
# Real language
|
||
print(predict_output("a = 6\nb = 7\nprint(a * b)"))
|
||
# → 42
|
||
|
||
# Novel fake language
|
||
print(predict_output("STORE X := 10\nSTORE Y := 5\nSPEAK X + Y"))
|
||
# → 15
|
||
```
|
||
|
||
## Training
|
||
|
||
|
||
|
||
### Data
|
||
|
||
Training data was split between real and synthetic languages:
|
||
|
||
**Real languages (8,000 examples total, 2,000 each):**
|
||
- Python — clean semantics, baseline
|
||
- JavaScript — type coercion, implicit behaviors
|
||
- Lua — minimal syntax, sparse
|
||
- COBOL — verbose, English-like, no conventional syntax markers
|
||
|
||
**Synthetic languages (120,000 examples total):**
|
||
- 200 procedurally generated fake languages
|
||
- Each language has randomized keywords, operators, variable styles, and block delimiters
|
||
- Semantics are consistent within each language but syntax varies wildly across all 200
|
||
- Programs generated via a Python simulator — outputs are ground truth from actual execution
|
||
- Three complexity tiers: assign+print (30%), conditionals (40%), loops (30%)
|
||
|
||
The spec for each fake language is discarded after data generation. The model only ever sees `(code, output)` pairs — it never gets a syntax guide.
|
||
|
||
### Configuration
|
||
|
||
- **Base model:** Qwen/Qwen2.5-1.5B (base, not instruct)
|
||
- **Training method:** Full fine-tuning (no LoRA)
|
||
- **Loss masking:** Loss computed on output tokens only, not prompt
|
||
- **Precision:** BF16
|
||
- **Optimizer:** AdamW (lr=2e-5, weight_decay=0.01)
|
||
- **Scheduler:** Cosine with 3% warmup
|
||
- **Batch size:** 8 per device × 4 gradient accumulation = 32 effective
|
||
- **Epochs:** 3
|
||
- **Max sequence length:** 512 tokens
|
||
- **Hardware:** NVIDIA A100 SXM4 40GB
|
||
- **Training time:** 66.5 minutes
|
||
- **Training cost:** ~$0.82
|
||
|
||
## Supported Operations
|
||
|
||
The model reliably handles:
|
||
|
||
- Variable assignment and arithmetic
|
||
- Print / output statements
|
||
- Conditionals (if/else)
|
||
- While loops with accumulator patterns
|
||
- String output
|
||
- Basic error behavior (empty output when conditions not met)
|
||
|
||
It does not handle: functions, recursion, file I/O, complex data structures, pipes, or multi-line string manipulation. These may work in real languages due to Qwen's pretraining knowledge but are not guaranteed.
|
||
|
||
## Limitations
|
||
|
||
- No actual code execution — outputs are predictions, not guarantees
|
||
- If-without-else edge cases can produce hallucinated else branches
|
||
- COBOL numeric padding format is inconsistent
|
||
- Long programs (many steps) may degrade in accuracy as state complexity grows
|
||
- Novel fake languages with very unusual execution models (non-linear control flow, stack-based semantics) are untested
|
||
- Context window limits programs to ~512 tokens
|
||
|
||
## Why
|
||
|
||
The original motivation was to ask: can a language model learn what *execution* means as an abstract concept, independent of any specific language's syntax?
|
||
|
||
The novel fake language results suggest yes, at least for basic programs. The model sees `WONDER x > 10` for the first time and figures out it's a conditional. It sees `SCRIBBLE @x BECOMES 7` and figures out it's assignment. It doesn't know these keywords — it infers them from the structure of the code and the patterns it learned during training.
|
||
|
||
Whether this scales to more complex programs, more alien execution models, or larger languages is an open question.
|
||
|
||
## Model Lineage
|
||
|
||
CaaLM-v1 is the first model in the CaaLM series, and a spiritual successor to the [LaaLM project](https://huggingface.co/LaaLM).
|
||
|
||
- **LaaLM-v1** — T5-base fine-tuned to simulate Linux shell commands (external state)
|
||
- **LaaLM-exp-v1** — Qwen 3B fine-tuned for conversational Linux terminal emulation (internal state)
|
||
- **CaaLM-v1** — Qwen 1.5B fine-tuned for language-agnostic code output prediction (current)
|
||
|
||
## License
|
||
|
||
Apache 2.0 (inherited from Qwen 2.5 base model) |