Release v0.5.0 - Batch decoding and benchmarks#5
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Add batch decoding methods (decode_batch and decode_batch_lossy) to both Rust core tokenizer and Python bindings for parallel processing of multiple token lists.
Add benchmark suites for cl100k_base, Llama 3, and o200k_base tokenizers. Each script compares splintr performance against reference implementations (tiktoken, HuggingFace) across single/batch encoding and decoding operations. - benchmark_cl100k.py: GPT-4/GPT-3.5-turbo tokenizer benchmarks - benchmark_llama3.py: Llama 3 family tokenizer benchmarks - benchmark_o200k.py: GPT-4o tokenizer benchmarks Benchmarks measure throughput (MB/s, tokens/s) and latency across various text types (short, medium, long, code, multilingual) with visualization support via matplotlib charts.
Add benchmark suites for cl100k_base, Llama 3, and o200k_base tokenizers. Each script compares splintr performance against reference implementations (tiktoken, HuggingFace) across single/batch encoding and decoding operations. - benchmark_cl100k.py: GPT-4/GPT-3.5-turbo tokenizer benchmarks - benchmark_llama3.py: Llama 3 family tokenizer benchmarks - benchmark_o200k.py: GPT-4o tokenizer benchmarks Benchmarks measure throughput (MB/s, tokens/s) and latency across various text types (short, medium, long, code, multilingual) with visualization support via matplotlib charts.
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Summary
decode_batch()anddecode_batch_lossy()methods for parallel decoding using RayonChanges
New Features
Fixes
.versionfile not being updated in previous release__version__in Python package