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README.md

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 ---
+language:
+- en
 license: cc-by-nc-4.0
+size_categories:
+- 100K<n<1M
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: format
+    dtype: string
+  - name: subset
+    dtype: string
+  - name: question_id
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 268211
+  download_size: 463470592
+  dataset_size: 1085161053
+task_categories:
+- question-answering
+tags:
+- code
+- synthetic
+- sft
+pretty_name: KodCode LFM2.5
 ---
+
+# KodCode LFM2.5
+
+A preprocessed version of [KodCode-V1-SFT-R1](https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-R1)
+formatted for fine-tuning [LFM2.5-1.2B-Thinking](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking)
+with a 70% code-only / 30% CoT (chain-of-thought) mix.
+
+## Dataset Description
+
+- **Homepage:** [KodCode Project](https://kodcode-ai.github.io/)
+- **Original paper:** [KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding](https://arxiv.org/abs/2503.02951)
+- **Original dataset:** [KodCode/KodCode-V1-SFT-R1](https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-R1)
+- **Points of contact:** [Zhangchen Xu](mailto:zxu9@uw.edu) (original authors)
+
+### Dataset Summary
+
+This dataset is derived from KodCode-V1-SFT-R1 (CC BY-NC 4.0). For each of the 268,211 training
+rows, the question-answer pair is formatted into a flat chat template string using
+`<|im_start|>` / `<|im_end|>` markers and a `<|startoftext|>` prefix — the native format for
+LFM2.5-1.2B-Thinking.
+
+Each row is assigned to one of two formats:
+
+- **code-only (70%):** `question` → `r1_solution` (code response, no thinking trace)
+- **CoT (30%):** `question` → `conversations[-1]["value"]` (full response with `<think>` reasoning)
+
+The CoT selection is limited to texts ≤ 15,000 characters (guaranteed to fit within 4096 tokens
+at the observed minimum char-to-token ratio of 2.70).
+
+### Changes from the Original
+
+1. **Format conversion:** Original `conversations` field (list of `from`/`value` dicts) is flattened
+   into a single `text` string with `<|im_start|>` / `<|im_end|>` chat template.
+2. **Format selection:** Each row is assigned either `code-only` or `cot` format at a 70/30 ratio
+   (controlled random per shard).
+3. **Char pre-filtering:** CoT texts exceeding 15,000 characters fall back to code-only format
+   (instead of being dropped), since only ~50% of CoT texts fit within 4096 tokens.
+4. **Reduced columns:** Only 4 columns are kept: `text`, `format`, `subset`, `question_id`.
+
+## Data Fields
+
+| Field | Type | Description |
+|---|---|---|
+| `text` | `string` | Flattened chat text: `<\|startoftext\|><\|im_start\|>user\n{question}<\|im_end\|>\n<\|im_start\|>assistant\n{answer}<\|im_end\|>\n` |
+| `format` | `string` | `"code-only"` or `"cot"` |
+| `subset` | `string` | Original KodCode subset (e.g. `"Leetcode"`, `"Codeforces"`, `"Taco"`, etc.) |
+| `question_id` | `string` | Original question identifier from KodCode |
+
+### Column mapping to original dataset
+
+| This dataset | KodCode-V1-SFT-R1 |
+|---|---|
+| `text` (code-only) | `question` + `r1_solution` formatted with chat template |
+| `text` (CoT) | `question` + `conversations[-1]["value"]` formatted with chat template |
+| `subset` | `subset` |
+| `question_id` | `question_id` |
+| *(omitted)* | `solution`, `test`, `test_info`, `version`, `style`, `metadata`, `r1_pass_sequence`, `r1_correctness`, `gpt_pass_sequence`, `gpt_difficulty`, `gpt_pass_percentage`, `conversations` |
+
+## Data Splits
+
+| Split | Size |
+|---|---|
+| `train` | 268,211 rows |
+
+## Usage
+
+### Load with HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("kodcode_dataset", split="train")
+# or from parquet directly:
+ds = Dataset.from_parquet("kodcode_dataset/data/kodcode-lfm2.5.parquet")
+```
+
+### Load for SFT training (TRL)
+
+```python
+from trl import SFTTrainer
+
+trainer = SFTTrainer(
+    ...,
+    train_dataset=ds,
+    dataset_text_field="text",
+    max_seq_length=4096,
+    packing=True,
+)
+```
+
+### Subset distribution
+
+```python
+print(ds.to_pandas()["subset"].value_counts())
+```
+
+### Format distribution
+
+```python
+print(ds.to_pandas()["format"].value_counts())
+# code-only    187824
+# cot           80387
+```
+
+## Statistics
+
+| Metric | Value |
+|---|---|
+| Total rows | 268,211 |
+| Code-only rows | 187,824 (70.0%) |
+| CoT rows | 80,387 (30.0%) |
+| Total characters | ~1.09B |
+| Text length (mean) | 4,046 chars |
+| Text length (median) | 2,130 chars |
+| Text length (max) | 15,000 chars |
+| Output size (parquet) | 442 MB |
+
+## Citation
+
+If you use this dataset, please cite the original KodCode work:
+
+```bibtex
+@article{xu2025kodcode,
+      title={KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding},
+      author={Zhangchen Xu and Yang Liu and Yueqin Yin and Mingyuan Zhou and Radha Poovendran},
+      year={2025},
+      eprint={2503.02951},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2503.02951},
+}
+```
+
+## License
+
+This dataset is derived from [KodCode-V1-SFT-R1](https://huggingface.co/datasets/KodCode/KodCode-V1-SFT-R1)
+and is distributed under the same **CC BY-NC 4.0** license.
+
+- **Attribution:** You must give appropriate credit to the original KodCode authors.
+- **NonCommercial:** You may not use the material for commercial purposes.
+- **No additional restrictions:** You may not apply legal terms that restrict others from doing
+  anything the license permits.
+
+See the [full license text](https://creativecommons.org/licenses/by-nc/4.0/) for details.

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