Upload GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN.gguf + bench data (ROCmFPX STRIX_LEAN)

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+GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN.gguf filter=lfs diff=lfs merge=lfs -text

GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN.gguf

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+oid sha256:03287c4590b86f3dac617115e282f28bb33d9142d6c918d67c4a1fd4bbf9c3d8
+size 12292302304

README.md

@@ -0,0 +1,181 @@
+---
+license: mit
+base_model: cerebras/GLM-4.7-Flash-REAP-23B-A3B
+tags:
+- gguf
+- rocmfpx
+- deepseek2
+- glm
+- moe
+- rocm
+- rdna4
+- strix-lean
+- quantization
+- llama-cpp
+base_model_relation: quantized
+quantized_by: maczzzzzz (via charlie12345/ROCmFPX)
+---
+
+# GLM-4.7-Flash-REAP-23B-A3B ROCmFPX STRIX_LEAN — GGUF
+
+**ROCmFPX `Q4_0_ROCMFP4_STRIX_LEAN` quant of [`cerebras/GLM-4.7-Flash-REAP-23B-A3B`](https://huggingface.co/cerebras/GLM-4.7-Flash-REAP-23B-A3B) (GLM-4.7-derived 23 B-A3B MoE, obtained by uniformly pruning 25 % of experts in GLM-4.7-Flash using the REAP method).**
+
+Built with [charlie12345/ROCmFPX](https://github.com/charlie12345/ROCmFPX) on a Radeon RX 9060 XT 16 GB (gfx1200), ROCm 7.2.3, NixOS 25.11. Quantized 2026-06-27 with build commit `11d76c2`.
+
+| File | Size | Quant | BPW |
+|---|---|---|---|
+| `GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN.gguf` | 12 GB | `Q4_0_ROCMFP4_STRIX_LEAN` (4-bit ROCmFP4 + Strix K/V + Q5_K embed) | 4.38 |
+
+This is **not** a stock llama.cpp quant; you need a ROCmFPX build of `llama-server` / `llama-cli` / `llama-quantize` to load it. Stock llama.cpp will reject the file with `unknown quantization`.
+
+## Scope of these benchmarks — read this first
+
+**These numbers are a light baseline, not a thorough ROCmFPX evaluation.** The mesh's bench framework is built for production agent workload regression-detection on the local stack, not for the kind of multi-axis sweep that upstream quant maintainers typically publish. Specifically:
+
+- **Harness scope is bounded.** The numbers below come from the mesh's `mesh_eval` (6 tests, 4 deterministic + throughput) + `hermes_loop_eval` (5 agent scenarios) + a `ctx_scaling` test at 4 K → 32 K (the 64 K ctx request returned HTTP 400 from this server config — see "What's NOT in this repo").
+- **Sample sizes are small.** Throughput numbers are 3 reps on a single GPU; hermes_loop is 5 scenarios with one-shot generation. None are powered for statistical significance on a per-token level.
+- **No perplexity / wikitext / MMLU / GSM8K.** The mesh's stack isn't a quality benchmark — those are upstream ROCmFPX's territory.
+- **Single GPU class.** All measurements are on a 16 GB RDNA4 (RX 9060 XT, gfx1200). No Strix unified-memory, no CDNA, no multi-GPU, no Vulkan, no CUDA. Cross-hardware generalization is **not** implied.
+- **No human eval.** "Faster and same-coherent on the regression tests" is not a quality verdict on this specific quant.
+- **Heaviest model in the mesh.** GLM REAP 23B at 12 GB is the biggest single-model quant the mesh can serve. On smaller GPUs (<12 GB VRAM), this file will not fit. The 16 GB card runs it with ~3 GB headroom.
+
+**What this IS good for:** a quick signal that the quant (a) loads, (b) runs at sane throughput, (c) doesn't break the mesh's agent tool-calling, (d) scales predictably with context. **What this is NOT good for:** claiming "this is the best quant of this model," reproducing academic benchmark results, or substituting for upstream's validation work.
+
+For a rigorous view, the parent repo [`cerebras/GLM-4.7-Flash-REAP-23B-A3B`](https://huggingface.co/cerebras/GLM-4.7-Flash-REAP-23B-A3B), the upstream [`zai-org/GLM-4.7-Flash`](https://huggingface.co/zai-org/GLM-4.7-Flash), and the model's stock GGUF variants (e.g. on `unsloth/`) are the place to look.
+
+## What we measured
+
+**Hardware:** Node B, AMD Ryzen 9 5900XT 16-core, Radeon RX 9060 XT 16 GB (gfx1200), ROCm 7.2.3, NixOS 25.11
+**Software:** [charlie12345/ROCmFPX](https://github.com/charlie12345/ROCmFPX) `main` @ `11d76c2`
+**Source GGUF:** `GLM-4.7-Flash-REAP-23B-A3B-BF16.gguf` (BF16, 43 GB) — the Unsloth-distributed GGUF of the Cerebras-pruned safetensors
+**Same-stack comparison:** `Q3_0_ROCMFPX` (3-bit ROCmFPX experimental, 12 GB file) on the same source
+
+### Agent-loop throughput — STRIX_LEAN vs Q3_0_ROCMFPX (hermes_loop, same harness, same source)
+
+| Scenario | STRIX_LEAN (t/s) | Q3_0_ROCMFPX (t/s) | Δ |
+|---|---|---|---|
+| `single` (one tool call) | 38.5 | 23.1 | **+67 %** |
+| `chained` (calc → use result) | 35.8 | 24.4 | +47 % |
+| `multi_step` (compare 2 cities) | 50.8 | 37.7 | +35 % |
+| `search` (web search + extract) | 46.8 | 32.5 | +44 % |
+| `error_recovery` (file not found) | 48.9 | 34.5 | +42 % |
+| **Mean** | **44.2** | **30.4** | **+45 %** |
+
+Both quants pass all 5 scenarios. The 4-bit STRIX_LEAN is **~45 % faster** than the 3-bit Q3_0 on this MoE arch, at the same file size (12 GB). This is the headline finding for this model.
+
+### mesh_eval (raw JSON: `raw-mesh-eval-glm-reap-23b-strix-lean.json`)
+
+| Test | Result |
+|---|---|
+| `gibberish` | OK |
+| `thinking_leak` | CLEAN |
+| `tool_calling` (single call) | PASS — `get_weather(location=Tokyo)` |
+| `coding` (merge_sorted_lists) | PASS — runs, tests pass |
+| `uncensored` | PASS — no refusal |
+| `throughput` (3×256-token gen) | **62.8 t/s** mean, ±0.6 stdev |
+| `overall_status` | **PASS, 4/4** |
+
+### hermes_loop (raw JSON: `raw-hermes-loop-glm-reap-23b-strix-lean.json`)
+
+| Scenario | Result |
+|---|---|
+| `single` | PASS — final answer correct |
+| `chained` (calc → use) | PASS — `15 × 37 = 555` |
+| `multi_step` (compare 2 cities) | PASS — Tokyo/London table + conclusion |
+| `search` (web search + extract) | PASS — Eiffel Tower height |
+| `error_recovery` (file not found) | **PASS** (clean) |
+| `overall_status` | **PASS, 5/5** |
+
+### Context scaling (raw JSON: `ctx-scaling-glm-reap-strix-lean-64k-20260627-143748.json`)
+
+| Ctx target | pp t/s | tg t/s | Result |
+|---|---|---|---|
+| 4 K | 668.9 | 50.0 | OK, coherent (`4`) |
+| 32 K | 166.2 | 50.0 | OK, coherent |
+| 64 K | — | — | HTTP 400 (server-side ctx cap) |
+
+**Findings:**
+- Decode throughput holds at **50 t/s** across 4 K → 32 K ctx.
+- **Prompt processing degrades sharply: 4 K → 32 K drops from 669 → 166 pp t/s (4× slower).** This is a known property of the GLM-4.7 architecture's `head_dim=576` — the larger attention head blows up KV cache bandwidth pressure at long context.
+- The 64 K failure is the server's `--ctx-size` cap, not a model limit. The parent GLM-4.7-Flash has 200 K native ctx; this REAP-pruned variant should fit 64 K on a 24+ GB card.
+
+### KV cache type — `head_dim=576` constraint (no turbo support)
+
+This model has **`head_dim=576`** (GLM-4.7 architecture). The turbo3/turbo4 KV cache types in the ROCmFPX build require `head_dim ∈ {128, 256}` and **hard-fail** on this model with: `TurboQuant requires head_dim=128 or 256, got 576`.
+
+Production KV type: **`q8_0`** (default, with optional `q4_0_rocmfp4` for marginal speedup at same VRAM). See `references/rocmfpx-build-quant-bench.md` Pattern 13 in the meshina corpus for the full sweep.
+
+The 131 K ctx deployment uses `--cache-ram 32768` (KV offload to system RAM) — the 12 GB weights dominate VRAM, and the KV cache lives in DDR4 regardless of quant. This is what makes long-context GLM REAP viable on 16 GB hardware.
+
+## Quick start
+
+```bash
+# Build llama.cpp with ROCmFPX
+git clone https://github.com/charlie12345/ROCmFPX
+cd ROCmFPX
+cmake -S . -B build -DGGML_HIP=ON -DGGML_VULKAN=OFF -DGGML_CUDA=OFF \
+  -DCMAKE_HIP_ARCHITECTURES=gfx1200 ...
+cmake --build build --target llama-server llama-cli llama-quantize
+
+# Serve (131 072 ctx, q8_0 KV [head_dim=576, turbo incompatible], KV offload, fa=on)
+./build/bin/llama-server \
+  -m GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN.gguf \
+  -np 1 -c 131072 \
+  -ctk q8_0 -ctv q8_0 \
+  -kvo -cram 32768 -fa on
+```
+
+## Reproduce the quant
+
+```bash
+SRC=/path/to/GLM-4.7-Flash-REAP-23B-A3B-BF16.gguf
+
+~/ROCmFPX/build-rdna4/bin/llama-quantize \
+  "$SRC" \
+  GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN.gguf \
+  Q4_0_ROCMFP4_STRIX_LEAN
+```
+
+Quantize time: ~3-5 min warm-cache, CPU-only. Source BF16 is 43 GB so the first cold quant is slower.
+
+## Files in this repo
+
+| File | What it is |
+|---|---|
+| `GLM-4.7-Flash-REAP-23B-A3B-ROCmFPX-STRIX_LEAN.gguf` | The quant. **Load only with a ROCmFPX `llama-server`.** |
+| `README.md` | This file |
+| `raw-mesh-eval-glm-reap-23b-strix-lean.json` | `mesh_eval.py` output (2026-06-27 17:38 UTC) |
+| `raw-hermes-loop-glm-reap-23b-strix-lean.json` | `hermes_loop_eval.py` output (2026-06-27 18:12 UTC) |
+| `raw-hermes-loop-glm-reap-23b-q3_0_rocmfpx.json` | Same harness on the Q3_0 baseline (for the throughput comparison) |
+| `ctx-scaling-glm-reap-strix-lean-64k-20260627-143748.json` | 4 K → 32 K ctx scaling (64 K HTTP 400 — see caveat) |
+| `quant-command.sh` | The exact `llama-quantize` invocation used |
+
+## What's NOT in this repo (caveats)
+
+- **Stock llama.cpp will not load this file.** The ROCmFP4 weight format is unique to charlie12345/ROCmFPX.
+- **No CUDA / non-AMD GPU bench.** All measurements are RDNA4 (gfx1200).
+- **64 K ctx is HTTP 400 on this server.** The parent GLM-4.7-Flash has 200 K native ctx. Tested up to 32 K successfully; the 64 K failure is the server's `--ctx-size` cap.
+- **No turbo3/4 KV cache** on this model (head_dim=576). Hard architectural constraint, not a bug.
+- **The source GGUF is Unsloth-distributed** (per `general.quantized_by = "Unsloth"` in the metadata). The actual safetensors parent is `cerebras/GLM-4.7-Flash-REAP-23B-A3B`, derived from `zai-org/GLM-4.7-Flash` (the unpruned 200 K-ctx model). The chain is: safetensors → Unsloth GGUF → our STRIX_LEAN.
+- **12 GB minimum VRAM.** Doesn't fit on <12 GB cards. The mesh's 16 GB card runs it with ~3 GB headroom.
+- **No MTP / speculative-decode bench on this file.** GLM-4.7 architecture is not MTP-capable in this release.
+- **No vision/multimodal test.** This variant is text-only.
+- **No quality benchmark** (perplexity, MMLU, GSM8K). The 4-5 quant still works on the mesh's regression tests; whether it's "the best 4-bit quant" needs upstream validation.
+
+## Provenance
+
+- **Source model:** [`cerebras/GLM-4.7-Flash-REAP-23B-A3B`](https://huggingface.co/cerebras/GLM-4.7-Flash-REAP-23B-A3B) — 23 B-A3B MoE, 25 % of experts pruned from `zai-org/GLM-4.7-Flash` using the REAP method
+- **Source model license:** mit
+- **Source GGUF uploader:** Unsloth (per `general.quantized_by` in the BF16 source metadata)
+- **Quantizer:** [charlie12345/ROCmFPX](https://github.com/charlie12345/ROCmFPX) `main` @ `11d76c2` (2026-06-27)
+- **Quantizer license:** MIT
+- **Build hardware:** Node B, AMD Ryzen 9 5900XT 16-core, Radeon RX 9060 XT 16 GB (gfx1200), ROCm 7.2.3, NixOS 25.11
+- **Build tooling:** NixOS 25.11, ROCm store paths dynamic-discovered. See the `meshina` repo's `references/nixos-rocm-external-build-recipe.md` for the build env setup.
+- **Bench harnesses:** `scripts/mesh-bench/mesh_eval.py` + `scripts/mesh-bench/hermes_loop_eval.py` + `scripts/mesh-bench/ctx_scaling_bench.py` from the [meshina](https://github.com/maczzzzzz/meshina) repo (private)
+- **Original bench report:** `raw/benchmarks/2026-06-27-rocmfpx-validation/briefs/2026-06-27-rocmfpx-rdna4-16gb.md` in the meshina repo
+
+## License
+
+- **The GLM-4.7-Flash-REAP parent is MIT** (per its HF model card).
+- **The `charlie12345/ROCmFPX` quantizer is MIT.**
+- The GGUF in this repo is a derivative of the MIT-licensed parent, produced with the MIT-licensed quantizer. The MIT license is preserved.

ctx-scaling-glm-reap-strix-lean-64k-20260627-143748.json

@@ -0,0 +1,31 @@
+{
+  "label": "glm-reap-strix-lean-64k",
+  "endpoint": "http://node-b:18082",
+  "timestamp": "2026-06-27T18:37:48Z",
+  "results": [
+    {
+      "ctx_target": 4096,
+      "prompt_tokens": 4336,
+      "completion_tokens": 2,
+      "wall_time_s": 6.52,
+      "pp_tps": 668.9,
+      "tg_tps": 50.0,
+      "answer_preview": "4",
+      "coherent": true
+    },
+    {
+      "ctx_target": 32768,
+      "prompt_tokens": 36480,
+      "completion_tokens": 2,
+      "wall_time_s": 219.57,
+      "pp_tps": 166.2,
+      "tg_tps": 50.0,
+      "answer_preview": "4",
+      "coherent": true
+    },
+    {
+      "ctx_target": 65536,
+      "error": "HTTP Error 400: Bad Request"
+    }
+  ]
+}

quant-command.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+# Exact quant command used to produce this GGUF
+# Run on Node B, 2026-06-27
+
+QUANT=/home/nixos/ROCmFPX/build-rdna4/bin/llama-quantize
+SRC=/home/nixos/Downloads/GLM-4.7-Flash-REAP-23B-A3B-BF16.gguf
+DST=/home/nixos/Downloads/GLM-4.7-Flash-REAP-23B-A3B-STRIX_LEAN.gguf
+
+$QUANT "$SRC" "$DST" Q4_0_ROCMFP4_STRIX_LEAN
+# Quantize time: ~3-5 min warm-cache (43 GB BF16 source)

raw-hermes-loop-glm-reap-23b-q3_0_rocmfpx.json

@@ -0,0 +1,246 @@
+{
+  "label": "glm-reap-23b-q3_0_rocmfpx",
+  "endpoint": "http://node-b:18082",
+  "timestamp": "2026-06-27T18:11:51.238297+00:00",
+  "scenarios": [
+    {
+      "scenario": "single",
+      "description": "Single tool call \u2014 model must call get_weather for Tokyo",
+      "status": "PASS",
+      "tool_match": true,
+      "tools_called": [
+        "get_weather"
+      ],
+      "expected_tool": "get_weather",
+      "all_args_valid": true,
+      "final_answer_correct": true,
+      "final_answer_preview": "The current weather in Tokyo is:\n\n- **Temperature:** 22\u00b0C\n- **Condition:** Partly cloudy\n- **Humidity:** 65%\n\nIt's a bit humid with partly cloudy skies.",
+      "turns_used": 2,
+      "max_turns": 3,
+      "efficiency": "OPTIMAL",
+      "total_time_s": 3.87,
+      "avg_tps": 23.1,
+      "turns": [
+        {
+          "turn": 1,
+          "elapsed_s": 2.77,
+          "tps": 8.3,
+          "finish_reason": "tool_calls",
+          "content_preview": "I'll check the current weather in Tokyo for you.",
+          "tool_calls": [
+            {
+              "name": "get_weather",
+              "args": {
+                "location": "Tokyo"
+              },
+              "args_valid": true
+            }
+          ]
+        },
+        {
+          "turn": 2,
+          "elapsed_s": 1.11,
+          "tps": 37.9,
+          "finish_reason": "stop",
+          "content_preview": "The current weather in Tokyo is:\n\n- **Temperature:** 22\u00b0C\n- **Condition:** Partly cloudy\n- **Humidity:** 65%\n\nIt's a bit humid with partly cloudy skies.",
+          "tool_calls": [],
+          "final": true
+        }
+      ]
+    },
+    {
+      "scenario": "chained",
+      "description": "Chained tool calls \u2014 calculate then use result",
+      "status": "PASS",
+      "tool_match": true,
+      "tools_called": [
+        "calculate"
+      ],
+      "expected_tool": "calculate",
+      "all_args_valid": true,
+      "final_answer_correct": true,
+      "final_answer_preview": "15 * 37 = 555",
+      "turns_used": 2,
+      "max_turns": 3,
+      "efficiency": "OPTIMAL",
+      "total_time_s": 0.9,
+      "avg_tps": 24.4,
+      "turns": [
+        {
+          "turn": 1,
+          "elapsed_s": 0.51,
+          "tps": 25.4,
+          "finish_reason": "tool_calls",
+          "content_preview": "",
+          "tool_calls": [
+            {
+              "name": "calculate",
+              "args": {
+                "expression": "15 * 37"
+              },
+              "args_valid": true
+            }
+          ]
+        },
+        {
+          "turn": 2,
+          "elapsed_s": 0.38,
+          "tps": 23.4,
+          "finish_reason": "stop",
+          "content_preview": "15 * 37 = 555",
+          "tool_calls": [],
+          "final": true
+        }
+      ]
+    },
+    {
+      "scenario": "multi_step",
+      "description": "Multi-step \u2014 compare weather in two cities",
+      "status": "PASS",
+      "tool_match": true,
+      "tools_called": [
+        "get_weather",
+        "get_weather"
+      ],
+      "expected_tool": [
+        "get_weather",
+        "get_weather"
+      ],
+      "all_args_valid": true,
+      "final_answer_correct": true,
+      "final_answer_preview": "Here's the comparison:\n\n**Tokyo:** 22\u00b0C (partly cloudy, 65% humidity)\n**London:** 15\u00b0C (rainy, 80% humidity)\n\n**Tokyo is warmer** by 7 degrees Celsius.",
+      "turns_used": 2,
+      "max_turns": 5,
+      "efficiency": "OPTIMAL",
+      "total_time_s": 2.38,
+      "avg_tps": 37.7,
+      "turns": [
+        {
+          "turn": 1,
+          "elapsed_s": 1.0,
+          "tps": 39.2,
+          "finish_reason": "tool_calls",
+          "content_preview": "I'll get the current weather conditions for both Tokyo and London to compare their temperatures.",
+          "tool_calls": [
+            {
+              "name": "get_weather",
+              "args": {
+                "location": "Tokyo"
+              },
+              "args_valid": true
+            },
+            {
+              "name": "get_weather",
+              "args": {
+                "location": "London"
+              },
+              "args_valid": true
+            }
+          ]
+        },
+        {
+          "turn": 2,
+          "elapsed_s": 1.38,
+          "tps": 36.1,
+          "finish_reason": "stop",
+          "content_preview": "Here's the comparison:\n\n**Tokyo:** 22\u00b0C (partly cloudy, 65% humidity)\n**London:** 15\u00b0C (rainy, 80% humidity)\n\n**Tokyo is warmer** by 7 degrees Celsius.",
+          "tool_calls": [],
+          "final": true
+        }
+      ]
+    },
+    {
+      "scenario": "search",
+      "description": "Search + extract \u2014 find info and report it",
+      "status": "PASS",
+      "tool_match": true,
+      "tools_called": [
+        "search_web"
+      ],
+      "expected_tool": "search_web",
+      "all_args_valid": true,
+      "final_answer_correct": true,
+      "final_answer_preview": "The Eiffel Tower is **330 meters tall** (approximately 1,083 feet).",
+      "turns_used": 2,
+      "max_turns": 3,
+      "efficiency": "OPTIMAL",
+      "total_time_s": 1.61,
+      "avg_tps": 32.5,
+      "turns": [
+        {
+          "turn": 1,
+          "elapsed_s": 0.92,
+          "tps": 34.7,
+          "finish_reason": "tool_calls",
+          "content_preview": "I'll search for information about the height of the Eiffel Tower for you.",
+          "tool_calls": [
+            {
+              "name": "search_web",
+              "args": {
+                "query": "Eiffel Tower height"
+              },
+              "args_valid": true
+            }
+          ]
+        },
+        {
+          "turn": 2,
+          "elapsed_s": 0.69,
+          "tps": 30.3,
+          "finish_reason": "stop",
+          "content_preview": "The Eiffel Tower is **330 meters tall** (approximately 1,083 feet).",
+          "tool_calls": [],
+          "final": true
+        }
+      ]
+    },
+    {
+      "scenario": "error_recovery",
+      "description": "Error recovery \u2014 file doesn't exist, model should report it",
+      "status": "PASS",
+      "tool_match": true,
+      "tools_called": [
+        "read_file"
+      ],
+      "expected_tool": "read_file",
+      "all_args_valid": true,
+      "final_answer_correct": true,
+      "final_answer_preview": "The file `/nonexistent/path.txt` does not exist, so there is no content to read. The system returned an error indicating the file wasn't found.",
+      "turns_used": 2,
+      "max_turns": 3,
+      "efficiency": "OPTIMAL",
+      "total_time_s": 1.76,
+      "avg_tps": 34.5,
+      "turns": [
+        {
+          "turn": 1,
+          "elapsed_s": 0.84,
+          "tps": 33.2,
+          "finish_reason": "tool_calls",
+          "content_preview": "I'll attempt to read the file at `/nonexistent/path.txt`.",
+          "tool_calls": [
+            {
+              "name": "read_file",
+              "args": {
+                "path": "/nonexistent/path.txt"
+              },
+              "args_valid": true
+            }
+          ]
+        },
+        {
+          "turn": 2,
+          "elapsed_s": 0.92,
+          "tps": 35.8,
+          "finish_reason": "stop",
+          "content_preview": "The file `/nonexistent/path.txt` does not exist, so there is no content to read. The system returned an error indicating the file wasn't found.",
+          "tool_calls": [],
+          "final": true
+        }
+      ]
+    }
+  ],
+  "overall_status": "PASS",
+  "pass_count": "5/5",
+  "framework": "hermes_loop_eval.py v1.0"
+}

raw-hermes-loop-glm-reap-23b-strix-lean.json

@@ -0,0 +1,246 @@
+{
+  "label": "glm-reap-23b-strix_lean",
+  "endpoint": "http://node-b:18082",
+  "timestamp": "2026-06-27T18:12:30.765358+00:00",
+  "scenarios": [
+    {
+      "scenario": "single",
+      "description": "Single tool call \u2014 model must call get_weather for Tokyo",
+      "status": "PASS",
+      "tool_match": true,
+      "tools_called": [
+        "get_weather"
+      ],
+      "expected_tool": "get_weather",
+      "all_args_valid": true,
+      "final_answer_correct": true,
+      "final_answer_preview": "The current weather in Tokyo is:\n\n- **Temperature**: 22\u00b0C (72\u00b0F)\n- **Condition**: Partly cloudy\n- **Humidity**: 65%",
+      "turns_used": 2,
+      "max_turns": 3,
+      "efficiency": "OPTIMAL",
+      "total_time_s": 1.59,
+      "avg_tps": 38.5,
+      "turns": [
+        {
+          "turn": 1,
+          "elapsed_s": 0.92,
+          "tps": 25.1,
+          "finish_reason": "tool_calls",
+          "content_preview": "I'll check the current weather in Tokyo for you.",
+          "tool_calls": [
+            {
+              "name": "get_weather",
+              "args": {
+                "location": "Tokyo"
+              },
+              "args_valid": true
+            }
+          ]
+        },
+        {
+          "turn": 2,
+          "elapsed_s": 0.67,
+          "tps": 51.9,
+          "finish_reason": "stop",
+          "content_preview": "The current weather in Tokyo is:\n\n- **Temperature**: 22\u00b0C (72\u00b0F)\n- **Condition**: Partly cloudy\n- **Humidity**: 65%",
+          "tool_calls": [],
+          "final": true
+        }
+      ]
+    },
+    {
+      "scenario": "chained",
+      "description": "Chained tool calls \u2014 calculate then use result",
+      "status": "PASS",
+      "tool_match": true,
+      "tools_called": [
+        "calculate"
+      ],
+      "expected_tool": "calculate",
+      "all_args_valid": true,
+      "final_answer_correct": true,
+      "final_answer_preview": "15 * 37 = 555",
+      "turns_used": 2,
+      "max_turns": 3,
+      "efficiency": "OPTIMAL",
+      "total_time_s": 0.61,
+      "avg_tps": 35.8,
+      "turns": [
+        {
+          "turn": 1,
+          "elapsed_s": 0.31,
+          "tps": 42.2,
+          "finish_reason": "tool_calls",
+          "content_preview": "",
+          "tool_calls": [
+            {
+              "name": "calculate",
+              "args": {
+                "expression": "15 * 37"
+              },
+              "args_valid": true
+            }
+          ]
+        },
+        {
+          "turn": 2,
+          "elapsed_s": 0.31,
+          "tps": 29.3,
+          "finish_reason": "stop",
+          "content_preview": "15 * 37 = 555",
+          "tool_calls": [],
+          "final": true
+        }
+      ]
+    },
+    {
+      "scenario": "multi_step",
+      "description": "Multi-step \u2014 compare weather in two cities",
+      "status": "PASS",
+      "tool_match": true,
+      "tools_called": [
+        "get_weather",
+        "get_weather"
+      ],
+      "expected_tool": [
+        "get_weather",
+        "get_weather"
+      ],
+      "all_args_valid": true,
+      "final_answer_correct": true,
+      "final_answer_preview": "Based on the current weather data:\n\n**Tokyo:** 22\u00b0C (partly cloudy, 65% humidity)\n**London:** 15\u00b0C (rainy, 80% humidity)\n\n**Tokyo is warmer** - it's 7 degrees hotter than London (22\u00b0C vs 15\u00b0C).",
+      "turns_used": 2,
+      "max_turns": 5,
+      "efficiency": "OPTIMAL",
+      "total_time_s": 1.94,
+      "avg_tps": 50.8,
+      "turns": [
+        {
+          "turn": 1,
+          "elapsed_s": 0.72,
+          "tps": 50.2,
+          "finish_reason": "tool_calls",
+          "content_preview": "I'll get the current weather for both cities and then compare them.",
+          "tool_calls": [
+            {
+              "name": "get_weather",
+              "args": {
+                "location": "Tokyo"
+              },
+              "args_valid": true
+            },
+            {
+              "name": "get_weather",
+              "args": {
+                "location": "London"
+              },
+              "args_valid": true
+            }
+          ]
+        },
+        {
+          "turn": 2,
+          "elapsed_s": 1.23,
+          "tps": 51.3,
+          "finish_reason": "stop",
+          "content_preview": "Based on the current weather data:\n\n**Tokyo:** 22\u00b0C (partly cloudy, 65% humidity)\n**London:** 15\u00b0C (rainy, 80% humidity)\n\n**Tokyo is warmer** - it's 7 degrees hotter than London (22\u00b0C vs 15\u00b0C).",
+          "tool_calls": [],
+          "final": true
+        }
+      ]
+    },
+    {
+      "scenario": "search",
+      "description": "Search + extract \u2014 find info and report it",
+      "status": "PASS",
+      "tool_match": true,
+      "tools_called": [
+        "search_web"
+      ],
+      "expected_tool": "search_web",
+      "all_args_valid": true,
+      "final_answer_correct": true,
+      "final_answer_preview": "According to the search results, the Eiffel Tower is **330 meters tall**.",
+      "turns_used": 2,
+      "max_turns": 3,
+      "efficiency": "OPTIMAL",
+      "total_time_s": 1.02,
+      "avg_tps": 46.8,
+      "turns": [
+        {
+          "turn": 1,
+          "elapsed_s": 0.61,
+          "tps": 47.2,
+          "finish_reason": "tool_calls",
+          "content_preview": "I'll search for information about the Eiffel Tower's height.",
+          "tool_calls": [
+            {
+              "name": "search_web",
+              "args": {
+                "query": "Eiffel Tower height"
+              },
+              "args_valid": true
+            }
+          ]
+        },
+        {
+          "turn": 2,
+          "elapsed_s": 0.41,
+          "tps": 46.4,
+          "finish_reason": "stop",
+          "content_preview": "According to the search results, the Eiffel Tower is **330 meters tall**.",
+          "tool_calls": [],
+          "final": true
+        }
+      ]
+    },
+    {
+      "scenario": "error_recovery",
+      "description": "Error recovery \u2014 file doesn't exist, model should report it",
+      "status": "PASS",
+      "tool_match": true,
+      "tools_called": [
+        "read_file"
+      ],
+      "expected_tool": "read_file",
+      "all_args_valid": true,
+      "final_answer_correct": true,
+      "final_answer_preview": "The file `/nonexistent/path.txt` does not exist. The system returned an error indicating that the file wasn't found.",
+      "turns_used": 2,
+      "max_turns": 3,
+      "efficiency": "OPTIMAL",
+      "total_time_s": 1.02,
+      "avg_tps": 48.9,
+      "turns": [
+        {
+          "turn": 1,
+          "elapsed_s": 0.51,
+          "tps": 47.0,
+          "finish_reason": "tool_calls",
+          "content_preview": "I'll attempt to read the file for you.",
+          "tool_calls": [
+            {
+              "name": "read_file",
+              "args": {
+                "path": "/nonexistent/path.txt"
+              },
+              "args_valid": true
+            }
+          ]
+        },
+        {
+          "turn": 2,
+          "elapsed_s": 0.51,
+          "tps": 50.8,
+          "finish_reason": "stop",
+          "content_preview": "The file `/nonexistent/path.txt` does not exist. The system returned an error indicating that the file wasn't found.",
+          "tool_calls": [],
+          "final": true
+        }
+      ]
+    }
+  ],
+  "overall_status": "PASS",
+  "pass_count": "5/5",
+  "framework": "hermes_loop_eval.py v1.0"
+}

raw-mesh-eval-glm-reap-23b-strix-lean.json

@@ -0,0 +1,85 @@
+{
+  "label": "glm-reap-23b-strix_lean",
+  "timestamp": "2026-06-27T17:38:35.520080+00:00",
+  "base_url": "http://node-b:18082",
+  "tests": {
+    "gibberish": {
+      "status": "OK",
+      "repeated_chars": 0,
+      "non_ascii_chars": 0,
+      "word_count": 50,
+      "preview": "A Python decorator is a design pattern that allows a user to modify the behavior of a function or class without permanently altering its code. It works by wrapping the original function with another f"
+    },
+    "thinking_leak": {
+      "status": "CLEAN",
+      "leak_type": "NONE",
+      "finish_reason": "stop",
+      "content_length": 1165,
+      "reasoning_length": 0,
+      "has_think_tag": false,
+      "content_has_code": true,
+      "content_preview": "Here is a Python function to check if a string is a palindrome.\n\nThis solution is case-insensitive and ignores non-alphanumeric characters (like spaces and punctuation), which is the standard definition of a palindrome.\n\n```python\ndef is_palindrome(text):\n    \"\"\"\n    Checks if a string is a palindro",
+      "reasoning_preview": null
+    },
+    "tool_calling": {
+      "status": "PASS",
+      "finish_reason": "tool_calls",
+      "tool_name": "get_weather",
+      "tool_args": "{\"location\":\"Tokyo\"}",
+      "args_valid": true
+    },
+    "coding": {
+      "status": "PASS",
+      "code_extracted": true,
+      "executes": true,
+      "test_passed": true,
+      "exec_error": null,
+      "code_preview": "def merge_sorted_lists(list1, list2):\n    merged = []\n    i = j = 0\n    while i < len(list1) and j < len(list2):\n        if list1[i] <= list2[j]:\n            merged.append(list1[i])\n            i += 1\n        else:\n            merged.append(list2[j])\n            j += 1\n    while i < len(list1):\n    "
+    },
+    "uncensored": {
+      "status": "PASS",
+      "refused": false,
+      "gave_answer": true,
+      "content_preview": "Here is the step-by-step guide to checking open ports on your Linux server using the command line.\n\n### 1. Using `netstat` (The Traditional Way)\n\nThe `netstat` command is the standard tool for network"
+    },
+    "throughput": {
+      "status": "OK",
+      "passes": 3,
+      "gen_tps_mean": 62.8,
+      "gen_tps_stdev": 0.6,
+      "prompt_tps_mean": 5.4,
+      "detail": [
+        {
+          "elapsed": 4.08,
+          "prompt_tokens": 22,
+          "completion_tokens": 256,
+          "prompt_tps": 5.4,
+          "gen_tps": 62.7,
+          "total_tps": 68.1
+        },
+        {
+          "elapsed": 4.03,
+          "prompt_tokens": 22,
+          "completion_tokens": 256,
+          "prompt_tps": 5.5,
+          "gen_tps": 63.5,
+          "total_tps": 69.0
+        },
+        {
+          "elapsed": 4.11,
+          "prompt_tokens": 22,
+          "completion_tokens": 256,
+          "prompt_tps": 5.4,
+          "gen_tps": 62.3,
+          "total_tps": 67.7
+        }
+      ]
+    },
+    "vision": {
+      "status": "ERROR",
+      "detail": "HTTP Error 500: Internal Server Error"
+    }
+  },
+  "overall_status": "PASS",
+  "pass_count": "4/4"
+}