structured-output
qwen3-4b-structured-output-merged-stage-a-OBLITERATED-i1-GGUFqwen3-4b-structured-output-merged-stage-a-OBLITERATED-GGUFqwen3-4b-structured-output-merged-stage-a-OBLITERATEDGemma-2-2B-Instruct-structured-outputQwen2.5-Coder-3B-SFT-StructuredOutput-GGUFpllum-12b-structured-output-loraazma-deepseek-coder-1.3b-instruct-structured-output-peft-mergeqwen3-4b-structured-output-merged-stage-a
sharegpt-structured-output-json
ShareGPT-Formatted Dataset for Structured JSON Output
Dataset Description
This dataset is formatted in the ShareGPT style and is designed for fine-tuning large language models (LLMs) to generate structured JSON outputs. It consists of multi-turn conversations where each response follows a predefined JSON schema, making it ideal for training models that need to produce structured data in natural language scenarios.
Usage
This dataset can be used to train LLMs… See the full description on the dataset page: https://huggingface.co/datasets/Arun63/sharegpt-structured-output-json.Nemotron-RL-Instruction-Following-Structured-Outputs-v2
Dataset Description:
Split 1: Direct Generation tests the model’s ability to perform freeform text structured outputs on JSON, YAML, and XML data, varying the complexity and presentation of the schema.
Split 2: Diversified Tasks adds 2 additional output formats: TOML and CSV, while increasing problem types to Direct Extraction from document, Translation between formats, Multistep Translation from known data, Multistep Extraction from unrelated context, Schema-Only Generation for… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Instruction-Following-Structured-Outputs-v2.Nemotron-RL-instruction_following-structured_outputs
Dataset Description:
The Nemotron-RL-instruction_following-structured_outputs dataset tests the ability of the model to follow output formatting instructions under schema constraints under the JSON format. Each problem consists of three components: The document, output formatting Instruction (Schema), and question. The dataset varies the difficulty of each problem by varying the location of instructions, the comprehensiveness of instructions, the complexity of the schema, and… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-instruction_following-structured_outputs.sft-tool-calling-structured-output-v1
vericava/sft-tool-calling-structured-output-v1
Dataset to train (SFT) 3-20B LLMs for tool calling and structured outputs/classifications.
Includes contents in English as well as some Japanese.
Nemotron-RL-Instruction-Following-Structured-Outputs-v2-prompt-only
Nemotron-RL-Instruction-Following-Structured-Outputs-v2-prompt-only
Prompt-only extraction from nvidia/Nemotron-RL-Instruction-Following-Structured-Outputs-v2.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-RL-Instruction-Following-Structured-Outputs-v2-prompt-only.structured-output-sft-100k
Structured Output SFT (100K)
100,000 ShareGPT conversations demonstrating correct generation of structured data formats: JSON, YAML, CSV, XML, Markdown tables, JSON Schema, and OpenAPI fragments. Each example pairs a natural language specification with a valid, well-formed output.
Motivation
Structured output generation is among the most commercially critical LLM capabilities. Models fail in characteristic ways:
Invalid JSON: unclosed brackets, trailing commas… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/structured-output-sft-100k.