README.md

metadata

license: apache-2.0
language:
  - en
tags:
  - genomics
  - tool-calling
  - function-calling
  - qwen
  - mlx
pretty_name: CodeXomics-ToolCalling-v1 (V5 training data)

CodeXomics-ToolCalling-v1

This dataset contains the supervised tool-calling trajectories used to fine-tune CodeXomics-ToolAgent-4B-v1 (internally qwen3.5:4b-codexomics-tools-v5) for the CodeXomics genomics workbench (an AI-native genome browser; source: github.com/Scilence2022/CodeXomics). It is released as the reproducibility artifact for the corresponding paper section and is licensed under Apache-2.0.

Contents

• train.jsonl — 373 supervised examples
• valid.jsonl — 123 validation examples
• test.jsonl — 30 holdout examples
• manifest.json — dataset schema, eligibility metadata, registry hash, and leakage-control record
• token-stats.json — render-length statistics per split (3,072-token budget)

Each line is an MLX-LM tool-calling example: a system prompt, a user instruction, and assistant tool_calls in the OpenAI function-calling shape. Multi-step examples include the returned tool results and use the cross-round reference syntax {tool_name.path}.

Composition

Split	Single-call	Multi-step	Variants	Synthetic chains	Total
Train	85	18	10	260	373
Validation	66	8	4	45	123
Test	21	8	1	0	30

Source trajectories (678 records) were admitted only after passing: (1) strong-model semantic replay (DeepSeek V4 Flash), (2) fixture executability, (3) state verification for stateful records, and (4) benchmark leakage control. All 172 CodeXomics Benchmark tests are fingerprinted and excluded (benchmark_scope.use: leakage-gate-only).

Training schema

Six candidate tool schemas per request (gold tool always included), function descriptions truncated to 160 characters, parameter defaults preserved, semantically hard distractors, and a deterministic-randomized gold position.

Usage

huggingface-cli download <this-dataset> --local-dir codexomics-v5-data

Citation

@software{codexomics-toolagent-v1,
  title = {CodeXomics-ToolCalling-v1},
  author = {Song, Lifu},
  year = {2026},
  license = {Apache-2.0},
  publisher = {Hugging Face}
}