| --- |
| 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) |
|
|
