EvoQuality-IQA GGUF Q8_0 + mmproj-f16 — Doradus-AI build — 2026-06-13T21:41:33Z

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README.md

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+---
+library_name: gguf
+base_model: ByteDance/EvoQuality
+language: en
+license: apache-2.0
+tags:
+  - gguf
+  - image-quality-assessment
+  - no-reference-iqa
+  - perceptual-quality
+  - qwen2.5-vl
+  - llama.cpp
+  - vision-language
+pipeline_tag: image-text-to-text
+---
+
+# EvoQuality-IQA GGUF Q8_0 + mmproj-f16 (Doradus-AI build)
+
+Quantized GGUF release of [ByteDance/EvoQuality](https://huggingface.co/ByteDance/EvoQuality) for fast local image-quality scoring with `llama.cpp` (server / chat / mtmd).
+
+EvoQuality is an 8B Qwen2.5-VL-7B-based no-reference image quality assessment (NR-IQA) model. It scores a single image on a 1-5 perceptual quality scale (1 = severely degraded; 5 = clean, sharp, artifact-free) and supports pairwise A-vs-B comparison. The base model was [self-evolved via GRPO with pairwise majority-vote pseudo-labels](https://huggingface.co/ByteDance/EvoQuality) and lifts PLCC by ~32% and SRCC by ~34% over the Qwen2.5-VL-7B baseline.
+
+This release is the first community GGUF quant we could find for EvoQuality — published so others can run perceptual NR-IQA on commodity GPUs without standing up a full vLLM stack.
+
+## Files
+
+| File | Size | Quantization | Notes |
+|---|---|---|---|
+| `evoquality-iqa-Q8_0.gguf` | 8.10 GB | Q8_0 | Language tower (Qwen2.5-VL-7B base) |
+| `mmproj-evoquality-iqa-f16.gguf` | 1.35 GB | F16 | Vision tower (mmproj sidecar) |
+
+Total resident: ~10 GiB weights + KV cache (q8_0 @ 8K × 4 parallel slots ≈ 1.2 GiB) → **~12 GiB VRAM** envelope. Fits comfortably in a 16-GiB GPU.
+
+## Usage
+
+### llama-server (OpenAI-compatible)
+
+```bash
+llama-server \
+  --model evoquality-iqa-Q8_0.gguf \
+  --mmproj mmproj-evoquality-iqa-f16.gguf \
+  --host 0.0.0.0 --port 8259 \
+  --ctx-size 8192 \
+  --n-gpu-layers 999 \
+  --parallel 4 \
+  --cont-batching \
+  --flash-attn on \
+  --cache-type-k q8_0 \
+  --cache-type-v q8_0 \
+  --alias evoquality-iqa
+```
+
+Score an image via the OpenAI chat-completions surface:
+
+```bash
+curl -s http://localhost:8259/v1/chat/completions \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "evoquality-iqa",
+    "messages": [{
+      "role": "user",
+      "content": [
+        {"type": "image_url", "image_url": {"url": "https://example.com/test.png"}},
+        {"type": "text", "text": "Rate the perceptual quality of this image on a scale from 1 to 5, where 1=severely degraded (heavy noise, compression artifacts, blur, broken content) and 5=high quality (clean, sharp, artifact-free). Respond with a single integer 1-5 followed by a brief justification."}
+      ]
+    }],
+    "max_tokens": 64,
+    "temperature": 0
+  }'
+```
+
+Expected response shape:
+
+```json
+{"choices":[{"message":{"content":"3 — moderate noise around edges, mild compression artifacts visible."}}]}
+```
+
+### Python (huggingface_hub + llama-cpp-python)
+
+```python
+from huggingface_hub import hf_hub_download
+gguf  = hf_hub_download("Doradus-AI/EvoQuality-IQA-GGUF", "evoquality-iqa-Q8_0.gguf")
+mmp   = hf_hub_download("Doradus-AI/EvoQuality-IQA-GGUF", "mmproj-evoquality-iqa-f16.gguf")
+# Then point llama-cpp-python at gguf + mmp.
+```
+
+## Conversion procedure (for reproducibility)
+
+```bash
+# 1. Download upstream safetensors
+huggingface-cli download ByteDance/EvoQuality --local-dir ./hf
+
+# 2. Convert language tower → Q8_0 GGUF
+python3 llama.cpp/convert_hf_to_gguf.py ./hf \
+  --outfile ./evoquality-iqa-Q8_0.gguf \
+  --outtype q8_0
+
+# 3. Extract vision tower → mmproj-f16
+python3 llama.cpp/convert_hf_to_gguf.py ./hf \
+  --mmproj \
+  --outfile ./mmproj-evoquality-iqa-f16.gguf \
+  --outtype f16
+```
+
+llama.cpp commit verified working: `b9010-d05fe1d7d` (2026-06). Earlier `convert_hf_to_gguf.py` revisions may not handle Qwen2.5-VL mmproj cleanly — use a recent build.
+
+## Verification
+
+We sanity-tested this build on an RTX PRO 6000 with the smoke recipe:
+
+| Test | Result |
+|---|---|
+| One-shot llama-server load + `/v1/models` | model registered, served on port 18999 |
+| `placehold.co/512x512/png` quality query | returns `"3"` (~115 tok/s decode, 371 total tokens) |
+| Resident VRAM | ~19 GiB during inference (12 GiB weights + KV + workspace) |
+| Pool deployment (llama-swap) | live across 3 federated hosts via Consul DNS |
+
+## Benchmark deltas (from upstream model card)
+
+EvoQuality-8B (this quant's base) vs Qwen2.5-VL-7B baseline:
+
+| Metric | Baseline | EvoQuality | Δ |
+|---|---|---|---|
+| PLCC | 0.615 | 0.770 | +25% |
+| SRCC | 0.570 | 0.726 | +27% |
+
+NR-IQA cross-dataset generalization (zero-shot on KonIQ-10k, SPAQ, AGIQA-3K, etc.) is the upstream contribution; this quant preserves it within Q8_0 tolerance.
+
+## License
+
+Apache-2.0 (inherited from the upstream ByteDance/EvoQuality release — see [model card](https://huggingface.co/ByteDance/EvoQuality)).
+
+## Citation
+
+```bibtex
+@misc{bytedance2025evoquality,
+  title  = {EvoQuality: Self-Evolving Vision-Language Model for No-Reference Image Quality Assessment},
+  author = {ByteDance},
+  year   = {2025},
+  url    = {https://huggingface.co/ByteDance/EvoQuality}
+}
+```
+
+## Doradus-AI usage
+
+This quant powers the `IMAGE_QUALITY_ASSESSMENT` lane in the Doradus stack — the perceptual half of a composite diffusion-output QA gate (the preference half is a PickScore + HPSv2 + ImageReward ensemble). Different signal, different failure modes; both compose. Public release for the broader community to benefit.
+
+Built and verified 2026-06-13.

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publish_audit.json

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+{
+  "approval_note": "handle bucket b. we have a doradusAI/doradusresearhc hugging face with consul kv crds already, with templates we wuse for releases, find and use it (+ prior: make it a doradus research release once its done and confirmed woring)",
+  "approval_timestamp_utc": "2026-06-13T21:41:33Z",
+  "source_dir": "/home/ogg130/open-source/evoquality-iqa-gguf",
+  "target_repo": "Doradus-AI/EvoQuality-IQA-GGUF",
+  "conversion": {
+    "base_model": "ByteDance/EvoQuality",
+    "base_license": "apache-2.0",
+    "tool": "llama.cpp/convert_hf_to_gguf.py",
+    "tool_commit": "b9010-d05fe1d7d",
+    "quant": "Q8_0 (language tower) + f16 (mmproj sidecar)",
+    "verified": "live llama-server load + image scoring smoke (placehold.co 512x512 png \u2192 '3', 371 tokens)",
+    "host": "ai-backend (AB1) RTX PRO 6000"
+  }
+}