Datasets:

witcheer
/

hermes-pairing-bench

license: mit
task_categories:
  - text-generation
tags:
  - agent
  - agentic
  - hermes
  - codeact
  - tool-use
  - local-llm
  - gguf
  - rtx-5090
  - benchmark
pretty_name: Hermes Pairing — Agentic Benchmark for Local LLMs
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files: hermes_pairing.csv

Hermes Pairing — Agentic Benchmark for Local LLMs (Phase A)

How well does a local LLM drive an agent? This dataset holds results for pairing local models with Hermes Agent (NousResearch) — a CodeAct agent: the model acts by writing Python (execute_code) that orchestrates tools, not by emitting JSON function calls.

Generated with llm-bench-rig on an NVIDIA RTX 5090 (32GB), llama.cpp / GGUF, under Hermes's real ~3.5K-token system prompt.

Phase A (synthetic). A reproducible synthetic ranking of agentic capability — not a real-harness verdict. Phase B (running the top finishers through the actual Hermes Agent) is the validation step.

Leaderboard

Rank	Model	Pairing	codeact	multistep	instruction	long-ctx
1	Qwopus-GLM-18B (13GB)	92.65	94	75	94	100
2	Qwen3.6-27B (21GB)	92.38	99	100	100	71
3	Nemotron-Cascade-2-30B	90.50	99	50	92	100
4	Hermes-4.3-36B (21GB)	84.27	95	67	98	67¹

¹ Hermes-4.3-36B OOMs at 64K context on 32GB → 0 at that depth.

The four axes (Hermes Pairing Score = weighted sum, 0–100)

codeact (0.40) — code-as-action: writes Python orchestrating tools, executed in a sandbox (pass@1).
longcontext (0.25) — retrieve-and-use a needle in a long memory/tool-log context at 16K/32K/64K (per-depth, VRAM-aware; an OOM depth scores 0).
instruction (0.20) — compliance under the real Hermes prompt vs minimal (the delta is the heavy-prompt tax).
multistep (0.15) — loop stability: forced fetch→observe→act loops.

Per-depth long-context and the instruction delta are in hermes_pairing.csv.

Findings (honest)

#1 and #2 are a statistical tie (within ~±5% CI). Not "Qwopus won by 0.3."
An 18B frankenmerge competes with the bigger models — efficiency over raw size.
The lab's own model finishes last — Hermes-4.3-36B is the worst pairing for Hermes Agent (gap is real).
No model wins all four axes — best agent model depends on workload: Qwen 27B is the reasoning-loop/instruction king but weak at retrieval; Nemotron + Qwopus ace long-context; Nemotron is weakest at multi-step.
The 64K VRAM wall — a 36B Q4 can't hold 64K KV in 32GB; smaller models can. Size is a liability for long-context agents on a single card.

Method

llama.cpp / llama-server, GGUF, RTX 5090 32GB. Real Hermes prompt extracted from the open-source repo (static guidance, ~3.5K tokens). codeact runs under a light prompt (the real prompt conditions models into incremental tool-call markup that fights a one-shot block eval); the other three run under the real prompt. Decision-run sizes: codeact n≈100, instruction n≈50, longcontext n≈15×3 depths, multistep n≈12. Full code + methodology: https://github.com/notwitcheer/llm-bench-rig (lib/agentic/).

Models

KyleHessling1/Qwopus-GLM-18B-Healed · unsloth/Qwen3.6-27B-GGUF · bartowski/Nemotron-Cascade-2-30B-A3B-GGUF · NousResearch/Hermes-4.3-36B-GGUF