PRIMA
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tests/tool_call_regression.py: - 10 test cases covering 5 hermes tools: read_file, web_search, terminal, execute_code, delegate_task - Schema validation (OpenAI-compatible tool call format) - Argument validation (correct tool + expected args) - Parallel tool calling test (multiple tools in one response) - Dry-run mode for CI (schema validation without server) - Full server mode with latency tracking - Markdown report generation with results matrix - JSON results output for programmatic consumption - 95% accuracy threshold gate (exit code 1 on failure) benchmarks/tool-call-regression.md: - Results template with model/preset matrix - Tool coverage tracking table .gitea/workflows/smoke.yml: - Added dry-run tool call schema validation step
TurboQuant
KV cache compression for local inference on M4 Max MacBook Pro.
What
TurboQuant (Google, ICLR 2026) is a three-stage KV cache compression method:
- PolarQuant — WHT rotation + polar coordinates + Lloyd-Max codebook (~4.2x compression)
- QJL — 1-bit quantized Johnson-Lindenstrauss residual correction
- TurboQuant — PolarQuant + QJL = ~3.5 bits/channel, zero accuracy loss
Why
Unlock 64K-128K context on qwen3.5:27b within 32GB unified memory. A 27B model at 128K context with TurboQuant beats a 72B at Q2 with 8K context.
Status
See issues for current progress.
Roles
- Strago: Build spec author
- Cid: Implementation, benchmarks, deployment
- Locke: Research support, upstream watch
- John: Quality review
- Frankie: Coordination
Source Repos
- TheTom/llama-cpp-turboquant — llama.cpp fork with Metal
- TheTom/turboquant_plus — Reference impl, 511+ tests
- amirzandieh/QJL — Author QJL code (CUDA)
- rachittshah/mlx-turboquant — MLX fallback
Docs
- Project Status — Full project status and build specification