0.5.2

• feat: use avx2 to speedup dotProduct
• feat: use avx2 to speedup matmulF32

0.5.1

• feat: use avx2 to speedup matmulQ40 (by @DifferentialityDevelopment)

0.5.0

• feat: splitting attention layers into all nodes. 🎉 🎉 🎉
• fix: convert-llama.py supports different max_seq_len.

0.4.0

• feat: support for any number of threads.
• fix: support max kv cache length.
• feat: splitting RoPE into all nodes.

0.3.1

• Changed order of QKV synchronization (details)
• All tasks of Llama architecture are executed in parallel
• Rope cache for Llama architecture

0.3.0

• New tokenizer format (old tokenizer files are not supported, please regenerate tokenizer files).
• Added Llama 3 support.
• Simple-server mode, check this example: nodejs-example.cjs Now you may use Distributed Llama as a simple LLM server.

0.2.0

Added Grok-1 support!

Breaking changes: you need to re-convert Llama 2 models to the new version.

0.1.1

This version introduces partial optimization for x86_64 AVX2 CPUs. Now it's possible to run the inference with Q40 weights and Q80 buffer with partial AVX2 acceleration.

0.1.0

Initial release! 🚢