Dolci Instruct SFT No Tools 200K: Supervised Fine-Tuning Dataset for Language Models

Dolci-Instruct-SFT-No-Tools-200K is a 200,000-instruction dataset for supervised fine-tuning of language models, published by the mlx-community on Hugging Face. The dataset is designed for training models without tool-use capabilities, as indicated by its title. It was last updated on January 30, 2026.

Use Cases

• Instruction-tuning of language models based on the 200,000 instruction-response pairs.
• Supervised fine-tuning for models without tool-use capabilities based on the 'No Tools' specification.
• Training models for general-purpose instruction following based on the dataset's intended use for SFT.
• Benchmarking fine-tuning techniques using a large-scale instruction dataset.

Strengths

• Dataset contains 200,000 instruction-response pairs for training.
• Published by the mlx-community, a known group on the Hugging Face platform.
• Last updated on January 30, 2026, indicating recent maintenance.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect bias inherent to its unspecified source and collection method.

Provenance

Source

mlx-community on Hugging Face

Collection Method

Likely collected or synthesized for supervised fine-tuning of language models.

Time Range

null

Freshness

Last updated 2026-01-30 23:58:56.

Geography

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