README.md

---
license: mit
tags:
- algo-sft-eval-redo
- cellular_automata
- algo
dataset_info:
  features:
  - name: question_id
    dtype: string
  - name: split
    dtype: string
  - name: domain
    dtype: string
  - name: task
    dtype: string
  - name: prompt
    dtype: string
  - name: model_response
    dtype: string
  - name: extracted_answer
    dtype: string
  - name: ground_truth
    dtype: string
  - name: correct
    dtype: bool
  - name: finish_reason
    dtype: string
  - name: token_count
    dtype: int64
  splits:
  - name: train
    num_bytes: 4198110
    num_examples: 2000
  download_size: 583523
  dataset_size: 4198110
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# algo-sft-eval-traces-cellular-automata-step-simulation-d5-v4

Full eval traces for algo-sft-cellular-automata-step-simulation-d5 across test/harder/ood splits

## Dataset Info

- **Rows**: 1500
- **Columns**: 11

## Columns

| Column | Type | Description |
|--------|------|-------------|
| question_id | Value('string') | Unique question identifier from eval set |
| split | Value('string') | Evaluation split: test (in-distribution), harder (scaled up), ood (structural out-of-distribution) |
| domain | Value('string') | Task domain: formal_logic, conlang_morphology, cellular_automata, long_arithmetic |
| task | Value('string') | Specific task variant (e.g., formal_logic_bottom_up) |
| prompt | Value('string') | Full prompt sent to the model |
| model_response | Value('string') | Complete untruncated model output |
| extracted_answer | Value('string') | Answer extracted by domain-specific parser |
| ground_truth | Value('string') | Expected correct answer |
| correct | Value('bool') | Whether extracted_answer matched ground_truth |
| finish_reason | Value('string') | vLLM finish reason: stop (natural end) or length (hit max_tokens) |
| token_count | Value('int64') | Number of tokens in model_response |


## Generation Parameters

```json
{
  "script_name": "eval_model.py",
  "model": "reasoning-degeneration-dev/algo-sft-cellular-automata-step-simulation-d5",
  "description": "Full eval traces for algo-sft-cellular-automata-step-simulation-d5 across test/harder/ood splits",
  "hyperparameters": {
    "max_tokens": 32768,
    "max_model_len": 32768,
    "temperature": 0.0,
    "base_model": "Qwen/Qwen2.5-1.5B-Instruct"
  },
  "input_datasets": []
}
```

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("raca-workspace-v1/algo-sft-eval-traces-cellular-automata-step-simulation-d5-v4", split="train")
print(f"Loaded {len(dataset)} rows")
```

---

*Uploaded via [RACA](https://github.com/Zayne-sprague/Dr-Claude-Code) hf_utility.*