0.0.0-alpha.9

• Added Gemma4 support with stronger per-layer transformer configuration.
• Introduced KV cache quantization, plus QJL quantization for key residuals.
• Expanded OpenAI API compatibility with missing endpoints, fields, response objects, and error formats.
• Added system fingerprint generation for chat completion model identification.
• Improved sampling controls with logprobs, top_logprobs, logit_bias, and repetition window support.

New Features

• Gemma4 architecture support and related model-loading/config upgrades.
• KV cache quantization path for reduced memory usage.
• QJL key-residual quantization support in the KV pool (--qjl-quantization).
• Repetition-window control for improved anti-repetition behavior.
• Extended sampling outputs to return token logprobs and top-logprobs.
• System fingerprint in chat completion responses.
• Broader OpenAI-compatible API surface and schema-aligned responses.

Performance and Efficiency

• Quantized KV pool handling for lower memory footprint.
• Deferred-write and allocator updates for quantized cache paths.
• Separate key/value quantization size handling for tighter memory control.
• End-to-end propagation of quantization settings through loader/manager/scheduler flow.

Reliability and Correctness

• Improved OpenAI-style error response formatting and route behavior.
• Better tokenizer handling for special tokens and chat templates.
• Stronger parser/config handling for advanced per-layer model settings.

Refactors and Maintainability

• Removed unused bytes_per_head from KvQuantizer.
• Internal cleanup around sampling output structures and KV quantization flow.

Dependencies

• Updated windows-sys dependency.

Full Changelog: 0.0.0-alpha.8...0.0.0-alpha.9