5 Commits
Author SHA1 Message Date
lukaszraczylo 1310c5bebe feat(llm): make runs resilient and reproducible on slow backends
Retry with exponential backoff and jitter, and honour Retry-After. Retry empty-choice responses and HTTP 408.
Add max_retries, retry_timeouts, model_timeouts and model_concurrency settings for slow local models.
Add opt-in SSE streaming with an idle timeout. A hung server fails early, and a slow but active generation continues.
Send an optional seed and record the backend fingerprint of each answer.
Stamp saved artifacts with run metadata and write them atomically. Warn in compare when two runs differ.
Save partial results on Ctrl-C. Turn an evaluator panic into an error row.
Reject duplicate models and non-http endpoints in the config.
2026-10-02 01:01:14 +01:00
lukaszraczylo cde7c7ce14 Update 2026-08-30 12:37:38 +01:00
lukaszraczylo 28aeb63dd8 ci: add goreleaser and semver-driven release workflow
The workflow calculates the version with semver-generator, tags the
commit when the version is new, and runs goreleaser. goreleaser ships
two distribution paths: tar.gz binaries for darwin and linux on amd64
and arm64, and a multi-arch ghcr.io container that carries the go,
python3, and C toolchains so the exec evaluators run in-container.
The new llmtest version subcommand reports the stamped build version.
2026-08-17 10:49:35 +01:00
lukaszraczylo ace50757e4 fix: floor token budgets and surface truncation, add live progress
Reasoning models burn thousands of completion tokens before the
answer. Small per-test caps caused silent truncation and zero scores.
The runner now floors max_tokens at the config default (12000). The
client parses finish_reason. Reports mark truncated cells and the
JSON format carries response text for debugging. A stderr progress
reporter prints one line per completed request; --quiet disables it.
2026-08-17 01:55:14 +01:00
lukaszraczylo 1d51d4cbbd feat: LLM accuracy testing framework with 17-test catalog
Framework runs a category/subcategory test catalog against multiple
LLMs through an OpenAI-compatible endpoint. Deterministic evaluators
score each response. Reports render as table, markdown, or JSON.
2026-08-17 01:29:45 +01:00