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README.md

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+---
+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](https://hermes-agent.nousresearch.com) (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](https://github.com/notwitcheer/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](https://huggingface.co/KyleHessling1/Qwopus-GLM-18B-Healed) ·
+[unsloth/Qwen3.6-27B-GGUF](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF) ·
+[bartowski/Nemotron-Cascade-2-30B-A3B-GGUF](https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-2-30B-A3B-GGUF) ·
+[NousResearch/Hermes-4.3-36B-GGUF](https://huggingface.co/NousResearch/Hermes-4.3-36B-GGUF)

hermes_pairing.csv

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+model,params,size_gb,pairing_score,codeact,multistep,instruction,longcontext,lc_16k,lc_32k,lc_64k,instruction_delta,hf_repo
+Qwopus-GLM-18B,18B frankenmerge,13,92.65,94.0,75.0,94.0,100.0,100.0,100.0,100.0,-28.0,KyleHessling1/Qwopus-GLM-18B-Healed
+Qwen3.6-27B,27B dense,21,92.38,99.0,100.0,100.0,71.11,73.33,66.67,73.33,-2.0,unsloth/Qwen3.6-27B-GGUF
+Nemotron-Cascade-2-30B,30B-A3B MoE,18,90.50,99.0,50.0,92.0,100.0,100.0,100.0,100.0,6.0,bartowski/nvidia_Nemotron-Cascade-2-30B-A3B-GGUF
+Hermes-4.3-36B,36B dense,21,84.27,95.0,66.67,98.0,66.67,100.0,100.0,0.0,2.0,NousResearch/Hermes-4.3-36B-GGUF