Evaluate language models with lm-eval-harness and build a custom benchmark 💻📓
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Updated
Oct 14, 2026 - Python
Evaluate language models with lm-eval-harness and build a custom benchmark 💻📓
Lightweight NLP/LLM evaluation toolkit — metrics, LLM-as-Judge, significance testing, prompt robustness
Author verifiable eval records through a draft → review → revise → submit loop with server-enforced graders; compile to JSONL/CSV/Inspect/lm-eval via MCP. STDIO or Streamable HTTP.
Task-dependent benchmark gap between /v1/completions and /v1/chat/completions on instruction-tuned LLMs -- two case studies on Qwen3.x GGUFs, reproduction recipe, and probes.
Fine-tuning pipeline for Qwen2.5-Coder-1.5B using QLoRA, DPO alignment, and AWQ quantization on free-tier Colab GPU.
Offline Self-Evolve pipeline using synthetic data generation — evaluate before/after with Rule+LLM Judge and lm-eval harness in measured workloads.
A custom lm-eval adapter built from scratch — implementing the full LM interface against Ollama's native API rather than relying on any off-the-shelf integration.
Custom evaluation harness with domain-specific benchmarks for enterprise LLMs
Evaluation framework for self-hosted LLMs. Systematic prompt ablation (baseline, CoT, few-shot, self-consistency voting) on Llama 3.1 8B via lm-evaluation-harness, with Wilson CI statistical analysis, determinism validation, and load testing under concurrency. Found chain-of-thought degrades accuracy 25pp at small scale.
Apple Silicon LLM benchmark harness for local runtimes and OpenAI-compatible endpoints: speed, memory, eval coverage, and public reports.
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