Add dataset card

README.md

@@ -1,4 +1,21 @@
 ---
+license: other
+license_name: source-specific-terms
+license_link: https://huggingface.co/datasets/inference-optimization/RazorCal/blob/main/LICENSE-DATA
+language:
+- en
+- zh
+size_categories:
+- 1K<n<10K
+task_categories:
+- text-generation
+tags:
+- calibration
+- model-compression
+- pruning
+- mixture-of-experts
+- moe
+- arxiv:2609.30465
 dataset_info:
   features:
   - name: domain
@@ -51,3 +68,158 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# RazorCal
+
+RazorCal is a general-purpose calibration corpus released with [RAZOR](https://arxiv.org/abs/2609.30465). It contains 2,048 samples across seven domains (about 8 MB). Each record includes input messages and source attribution. The records carry no architecture-specific fields, so the corpus also suits FFN or layer pruning, perplexity measurement and other calibration-based compression; RAZOR uses it to estimate per-expert statistics. Reasoning traces are omitted because the calibration loader does not consume them. Potential contact identifiers, home-directory usernames, private-network examples and literal password examples are replaced with placeholders.
+
+This repository re-publishes the corpus in a row-oriented format: each of the 2,048 samples is one dataset row. It is converted from the original single-file release at [Nickyang/RazorCal](https://huggingface.co/datasets/Nickyang/RazorCal), which is also included unchanged at [source/RazorCal.json](source/RazorCal.json) so that the conversion can be verified against the original artifact.
+
+## Composition
+
+| Domain | Samples |
+|---|---:|
+| Coding | 512 |
+| Math | 256 |
+| Science (STEM) | 256 |
+| Chinese-STEM | 256 |
+| Instruction Following | 256 |
+| Tool Calling | 256 |
+| World Knowledge | 256 |
+| **Total** | **2,048** |
+
+## Format
+
+Each row is one sample with the following columns:
+
+| Column | Type | Description |
+|---|---|---|
+| `domain` | string | one of the seven domains above |
+| `sub_source` | string | recorded upstream source attribution |
+| `coding_subclass` | string, nullable | subclass label, present on Coding samples |
+| `messages` | list of structs | chat messages with `role`, `content` and optional `tool_calls` (`id`, `type`, `index`, `function.name`, `function.arguments`) |
+| `tools` | list of structs, nullable | tool definitions with `type` and `function` (`name`, `description`, `parameters`) |
+
+The corpus defines a single `train` split holding all 2,048 samples; no train/validation/test semantics are implied.
+
+Conversions applied to the original file, needed to keep a single valid arrow schema:
+
+- `tool_calls[].function.arguments` and `tools[].function.parameters` are free-form JSON objects whose value types conflict across records (for example a string and a number under the same key path of different records). They are recorded as JSON strings; parse them with `json.loads` before use.
+- The optional message fields `tool_call_id` and `name` are null for every record in the original file and are omitted here.
+- Empty `tool_calls` and `tools` lists are recorded as null.
+
+Everything else is preserved verbatim. The corpus metadata recorded in the original file's `meta` object:
+
+```json
+{
+  "name": "RazorCal",
+  "total_samples": 2048,
+  "max_length_per_sample": 32000,
+  "domains": {
+    "Math": 256,
+    "Science (STEM)": 256,
+    "Chinese-STEM": 256,
+    "Instruction Following": 256,
+    "Tool Calling": 256,
+    "World Knowledge": 256,
+    "Coding": 512
+  },
+  "coding_subclass_distribution": {
+    "swe_issue_fix": 40,
+    "engineering_oop_api": 111,
+    "cp_python_stdin": 138,
+    "cp_cpp_stdin": 99,
+    "func_signature_completion": 100,
+    "leetcode_class_solution": 24
+  },
+  "homepage": "https://github.com/nick7nlp/RAZOR",
+  "description": "RazorCal is a multi-domain calibration corpus released with RAZOR. It is a general-purpose calibration mixture: the records carry no architecture-specific fields and can be used for expert, FFN or layer pruning as well as other calibration-based compression. Source labels record attribution; see the datasheet for provenance, privacy processing, and usage limitations.",
+  "license": "Source-specific terms apply; see LICENSE-DATA.",
+  "privacy_processing": "Reasoning traces are omitted. Contact identifiers, home-directory usernames, private-network examples, and literal password examples are replaced with placeholders."
+}
+```
+
+Loading the rows:
+
+```python
+import json
+
+from datasets import load_dataset
+
+dataset = load_dataset("inference-optimization/RazorCal", split="train")
+
+sample = dataset[0]
+messages = sample["messages"]
+for call in messages[-1]["tool_calls"] or []:
+    call["function"]["arguments"] = json.loads(call["function"]["arguments"])
+```
+
+For the [RAZOR CLI](https://github.com/nick7nlp/RAZOR), use the unchanged original file:
+
+```bash
+curl -L -o RazorCal.json \
+  https://huggingface.co/datasets/inference-optimization/RazorCal/resolve/main/source/RazorCal.json
+
+razor saliency --model <model-path> --data RazorCal.json --out out/sal
+```
+
+## Recorded sources
+
+A source label records attribution information; it is not, by itself, proof
+of a record-level match to an upstream release.
+
+| Domain | `sub_source` labels and counts | Recorded source description |
+|---|---|---|
+| Math | `nemotron-cascade2-math` (160); `nemotron-math-v2-high-aime-style` (96) | Nemotron-Cascade-2-SFT-Data math slice; Nemotron-Math-V2 |
+| Science (STEM) | `Nemotron-Science-V1-RQA` (176); `Nemotron-Science-V1-MCQ` (80) | Nemotron-Science-V1 |
+| World Knowledge | `OpenHermes-2.5` (256) | OpenHermes-2.5 via smoltalk2 |
+| Coding | `Dolci-Python-Algorithms` (205); `competitive-programming-python` (141); `competitive-programming-cpp` (115); `Nemotron-SFT-SWE-V2-agentless` (40); `Nemotron-OpenCode-general` (11) | Dolci; competitive-programming, SWE and OpenCode corpora |
+| Instruction Following | `Dolci-Precise-IF` (256) | Dolci instruction-following corpus |
+| Tool Calling | `Nemotron-Agentic-tool-calling-v2` (256) | Nemotron agentic/tool-calling corpus |
+| Chinese-STEM | `multilingual-chinese-math` (86); `multilingual-chinese-code` (85); `multilingual-chinese-stem` (85) | Multilingual Chinese categories |
+
+The `nemotron-cascade2-math` records were taken as a whole domain slice rather
+than sampled individually, so the label records the upstream dataset but not a
+per-record identifier within it. Source labels alone do not establish
+provenance. See [FIDELITY_AUDIT.md](FIDELITY_AUDIT.md) for provenance
+boundaries and [LICENSE-DATA](LICENSE-DATA) for recorded license information.
+
+## Usage limitations
+
+RazorCal is calibration data, not an independent evaluation benchmark.
+Domain coverage does not guarantee preservation of model quality. Review
+possible overlap with evaluation data for the intended use.
+
+The original record array contains 587 tool messages without populated
+`tool_call_id` links and 11 tool definitions with empty `parameters` objects;
+in this repository the always-null `tool_call_id` and `name` fields are
+omitted, as documented under [Format](#format). Tokenizer chat templates and
+schema validators may require additional handling at load time.
+
+Public source material can still contain personal references or unsuitable
+content; source labels do not establish that records are free of such content.
+See [DATASHEET.md](DATASHEET.md) for intended use and limitations.
+
+## Citation and licensing
+
+Source-specific terms continue to apply; no single replacement license is
+asserted for all RazorCal records. Retain applicable upstream attribution.
+See [LICENSE-DATA](LICENSE-DATA) and [NOTICE](NOTICE) before use or
+redistribution.
+
+This repository contains no code; [LICENSE-CODE](LICENSE-CODE) is retained
+from the upstream release for completeness. See [DATASHEET.md](DATASHEET.md)
+for intended use and limitations and [FIDELITY_AUDIT.md](FIDELITY_AUDIT.md)
+for the scope of available provenance evidence.
+
+```bibtex
+@misc{song2026razorpruningreplaceableexperts,
+      title={RAZOR: Pruning Replaceable Experts in LLMs},
+      author={Mingyang Song and Mao Zheng},
+      year={2026},
+      eprint={2609.30465},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2609.30465},
+}
+```