AI
AI-adjacent formats and workflows on Konvert.in — model files, embeddings, datasets and prompt bundles — with tools on the roadmap.
Format details
What is AI?
The AI family covers the formats that show up when you work with modern machine learning: model weight files (safetensors, GGUF, ONNX), tokenizer and configuration bundles, embedding stores and dataset formats like JSONL and Parquet. On Konvert, this hub is a map of the space rather than a full workshop — dedicated conversion utilities are future. The focus is on helping you understand what each file actually contains and how it fits into a typical model or dataset workflow. Live Konvert tools for this family are not available yet.
Specifications
- Typical file size
- Model weights range from tens of megabytes for small on-device models to tens or hundreds of gigabytes for large frontier models. Dataset shards commonly land in the hundreds of megabytes as JSONL or Parquet files.
- Quality
- In an AI context, quality is about numerical fidelity and metadata integrity. Precision (FP32, FP16, BF16, INT8, INT4) determines how much of the original model's behaviour is preserved after quantization. For datasets, quality means clean schemas, consistent labels and stable encoding, not visual sharpness.
- Transparency support
- Varies — Not applicable. Transparency has no meaningful analogue in model or dataset files.
- Animation support
- Varies — Not applicable. AI artifacts describe parameters or data, not animated media.
- Compression type
- Weight formats often use custom binary layouts with optional quantization. Dataset formats like Parquet include columnar compression (Snappy, ZSTD, GZIP). JSONL is uncompressed by default but compresses very well.
- Browser support
- Browsers can display metadata and text-based configs directly. Actual model inference in the browser is possible via WebGPU / WebGL / WebAssembly, but is bounded by memory and hardware.
- Compatibility
- safetensors and GGUF are widely used in the open-source model ecosystem. ONNX is a common runtime-agnostic format. JSONL is the standard for line-delimited datasets. Parquet is the go-to for large tabular datasets in analytics and ML pipelines.
Advantages
- Open formats like safetensors and GGUF make models portable across runtimes.
- Parquet and JSONL scale from small experiments to production-sized datasets.
- Quantization formats (INT8, INT4) enable running larger models on modest hardware.
- Standardized metadata makes reproducing experiments dramatically easier.
- Growing ecosystem of open tools for inspection, conversion and validation.
Disadvantages
- Model files are huge; casual conversion in the browser is limited by memory.
- Quantization is lossy — accuracy can degrade in ways that are hard to spot.
- Format churn is real: new weight and dataset formats appear regularly.
- Konvert.in does not yet ship live AI conversion tools, so this hub is descriptive.
- Security matters: never load untrusted model files that use pickle-based formats.
Common uses
- Preparing an open-source model for local inference on a laptop or workstation.
- Curating a JSONL dataset of prompts and responses for fine-tuning.
- Converting datasets to Parquet for faster analytics and training pipelines.
- Sharing prompt libraries and evaluation sets as version-controlled JSON.
- Exporting embeddings for use with vector databases.
Common conversions
- PyTorch checkpoints → safetensors (planned; safer, faster loading)
- safetensors ↔ GGUF for local inference runtimes
- JSONL ↔ Parquet for dataset storage
- Prompt/YAML bundles into normalised JSON
- Quantization between FP16 / INT8 / INT4 weights
Compression tips
- Prefer Parquet over CSV or JSONL for large tabular datasets — you'll get columnar compression for free.
- Compress JSONL with zstd for storage and transport; leave it uncompressed while iterating locally.
- Quantize models progressively (FP16 → INT8 → INT4) and evaluate quality at each step.
- Store model metadata (tokenizer configs, generation params) alongside weights, not embedded in filenames.
- Use deduplication and shuffling on large datasets before compression to improve ratios.
Frequently asked questions
The AI family currently focuses on explaining formats. Dedicated tools for model and dataset conversions are not available yet and will roll out gradually.
safetensors is a purpose-built format for storing tensors: it's faster to load, memory-mappable and does not execute code when loaded, which avoids the security risks of pickle-based formats.
GGUF is designed around efficient local inference of language models (especially with runtimes like llama.cpp). ONNX is a more general, cross-runtime format aimed at deploying many kinds of models across different backends.
Use JSONL for streaming, ad-hoc inspection and mixed schemas. Use Parquet when the schema is stable and you care about compression and analytical performance.
Prefer safetensors, GGUF or ONNX from reputable sources. Avoid loading untrusted pickle-based files, and check hashes and provenance whenever possible.