Instructions to use ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2
# Run inference directly in the terminal:
llama cli -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2
# Run inference directly in the terminal:
llama cli -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2
# Run inference directly in the terminal:
./llama-cli -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

Use Docker

docker model run hf.co/ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

LM Studio
Jan

vLLM

How to use ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

Ollama
How to use ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 with Ollama:
```
ollama run hf.co/ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2
```
Unsloth Desktop

How to use ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 with Docker Model Runner:
```
docker model run hf.co/ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2
```

Lemonade

How to use ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-DS4-IQ2-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

ivanfioravanti commited on Sep 7

Commit

b8b2039

verified ·

1 Parent(s): 672c52b

Replace IQ2 release with smaller padded Q2_K down model

Browse files

Publish the 41.73 GiB IQ2_XXS/Q2_K model with unchanged MTP and external PLE. Update model structure, runtime requirements, quality/speed measurements and checksums; supersede the 46.89 GiB MXFP4-down release.

Files changed (11) hide show

.gitattributes +1 -0
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README.md +125 -106
SHA256SUMS +5 -7
qwen38-iq2-quality-results.json +0 -0
qwen38-iq2-quality.md +0 -62
qwen38-q2down-results.json +645 -0
qwen38-q2down.md +93 -0
qwen38-vision-quality-results.json +0 -176
qwen38-vision-quality.md +0 -52

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Qwen3.8-Flash-Next-IQ2XXSImatrix-MXFP4Down-MTP.gguf.json → Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf.json RENAMED Viewed

@@ -1,5 +1,5 @@
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-  "template": "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-Q4KImatrix-MTP-qwen4exp-pleext.gguf",
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 }

README.md CHANGED Viewed

@@ -13,123 +13,142 @@ tags:
 - image-text-to-text
 ---
-# Qwen3.8-Flash-Next DS4 IQ2 (imatrix)
-A **50.34 GB (46.89 GiB)** mixed IQ2_XXS/MXFP4 quantization, including the
-MTP block, for [DS4 Metal](https://github.com/ivanfioravanti/ds4-metal/tree/qwen3.8-flash-next).
-The same GGUF supports ordinary decoding and optional `--mtp` speculation.
-The **external Q4_1 PLE sidecar is required** and is reused from the
-[Q4 release](https://huggingface.co/ivanfioravanti/Qwen3.8-Flash-Next-DS4-Q4).
-**64 GB testers wanted:** this is a candidate for a 64 GB Mac, not a confirmed
-fit. Tests below ran on an M3 Ultra with 512 GiB. Please report results in this
-repository's Community tab, including chip, RAM, macOS, DS4 commit, context,
-prefill chunk, MTP setting, memory pressure/swap, speed and any errors.
-Start at 8K or 32K context, then try 64K. Include the PLE and optional vision
-encoder in your report. Avoid sharing private prompts or data.
-## Recipe and calibration
-| Tensor group | Format |
-| --- | --- |
-| 96 trunk expert gate/up tensors (48 layers) | IQ2_XXS, imatrix calibrated directly from BF16 |
-| Trunk expert down projections | MXFP4 |
-| Dense GDN/QSA, shared experts and output | Q8_0, copied from Q4 template |
-| Embeddings, routers, norms, hyper-connections and control | Higher precision, copied unchanged from Q4 template |
-| Embedded MTP block | All 32 tensors copied unchanged from Q4 template |
-| Required external PLE | Q4_1, CPU mapped and demand-paged |
-Source: [Qwen/Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next),
-BF16 revision `de4b8e4d43b917e7706784d8bb445c9af86a3540`.
-Calibration uses the same pinned
-[Unsloth imatrix](https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF)
-as the Q4 build: revision `c8b5954a88c2775c546b92593eda40ea041d3176`, SHA-256
-`a5863123db1ca458727e738955bef7bfc199520aa2bee3a30142a1aff9254154`.
-The calibration dataset is `unsloth_calibration_Qwen3.8-Flash-Next.txt`,
-45 chunks of 18,432 tokens. Sixteen zero-count gate/up entries use the
-existing deterministic per-expert input-column weight-energy fallback.
-All other entries use the activation importance statistics.
-All 1,255 output tensors were read back and hash-verified. The adjacent
-`.gguf.json` contains the per-tensor source/output hashes and build provenance.
-The build tool is
-[`gguf-tools/qwen4_iq2.py`](https://github.com/ivanfioravanti/ds4-metal/blob/c64bdffc1028b4c30f1b48a325b3b7f4243a8bde/gguf-tools/qwen4_iq2.py).
-This is a DS4-targeted `qwen4exp` GGUF; compatibility with other runners is not claimed.
-## Download and run
-Build DS4 from the `qwen3.8-flash-next` branch (`make -j8 ds4 ds4-server`)
-and run from its repository root. Tested code revision: `c64bdff` includes
-the recorded results; the runtime was unchanged across the test commits.
 ```sh
-QWEN_IQ2_DIR="$HOME/models/qwen38-ds4-iq2"
 hf download ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 \
-  --local-dir "$QWEN_IQ2_DIR"
 hf download ivanfioravanti/Qwen3.8-Flash-Next-DS4-Q4 \
-  Qwen3.8-Flash-Next-PLE-Q4_1.gguf --local-dir "$QWEN_IQ2_DIR"
 ./ds4 --metal \
-  -m "$QWEN_IQ2_DIR/Qwen3.8-Flash-Next-IQ2XXSImatrix-MXFP4Down-MTP.gguf" \
-  --ple "$QWEN_IQ2_DIR/Qwen3.8-Flash-Next-PLE-Q4_1.gguf" \
-  --ctx 32768 --prefill-chunk 1024
 ```
-If you already downloaded the Q4 release, point `--ple` to that existing
-sidecar instead. Add `--mtp` for speculation; use `--mtp-exact-sampling`
-when preserving the target sampling distribution is required.
-For vision, run `./download_model.sh qwen38-vision` and add
-`--vision gguf/mmproj-Qwen3.8-Flash-Next-Q8_0.gguf` to your command.
-Then use `/read image.png` in the CLI, or image content in server API requests.
-The optional encoder is 0.617 GB; vision adds further runtime allocations.
-## Quality
-Same 50-case `ds4-eval --suite hard` run per recipe, 32K context, temperature
-0, seed 123, native token budgets, no speculation or retries:
-| Recipe | Passed | Wrong | Incomplete | BF16 top-1 agreement | Target NLL |
-| --- | ---: | ---: | ---: | ---: | ---: |
-| IQ2_XXS imatrix | 36/50 | 3 | 11 | 91.32% | 0.27828 |
-| Q4_K imatrix | 35/50 | 2 | 13 | 96.57% | 0.21287 |
-| Q4_0 routed | 35/50 | 0 | 15 | 96.52% | 0.20959 |
-| Q8 experts | 34/50 | 2 | 14 | 98.48% | 0.19828 |
-The BF16 comparison uses 85 tokenization-aligned short continuations, with
-the same 15 misaligned cases excluded for every model. Lower NLL is better.
-IQ2's similar hard-suite score **does not establish better or equal quality**:
-its probability drift is substantially higher. Q4_K versus IQ2 isolates the
-gate/up precision change; Q8 and Q4_0 use different complete recipes.
-IQ2 and Q4_K also passed **6/6 vision smoke cases** each: OCR, diagrams,
-spatial relations, screenshots, photo recognition and an unrelated-premise
-check. This is a small substring-graded API suite, not a broad vision benchmark
-or an HF encoder numerical-parity test.
-See [text results](qwen38-iq2-quality.md) and
-[vision results](qwen38-vision-quality.md), including their machine-readable data.
-## Memory and speed
-- Main model: **50,343,093,376 bytes**, including MTP.
-- Required PLE sidecar: approximately **32 GB on disk**, preferably on a fast SSD.
-  Its demand-paged CPU pages consume memory when resident; it is not zero-RAM storage.
-- At 64K context with a 1,024-token prefill chunk, model file size plus peak
-  tracked Metal runtime tensors totals **53.48 GB**. This is a **partial budget**:
-  resident PLE pages, other host/driver allocations and the OS are additional.
-- Ordinary decode measured **47.88 tokens/s at 32K** and **47.49 tokens/s at 64K**,
-  for 128 generated tokens on the M3 Ultra. These are not 64 GB Mac measurements.
-- A separate short-prompt greedy test at 64K allocated context matched ordinary
-  and MTP output byte-for-byte, accepting 120/135 drafts (88.9%). Full-context
-  MTP quality and memory pressure on physical 64 GB hardware remain untested.
-## Integrity and attribution
-Main GGUF SHA-256:
-`31f1e193771a5f3fdaa7af5865e0417513e8e8cac37579d388052ca54c889d4b`.
-`SHA256SUMS` covers every published release file except itself.
-Derived from Qwen's original model, with calibration from Unsloth and the DS4
-runtime/build tools. This derivative carries the **Qwen Community License 1.0**;
-the original copyright and full terms are included in [LICENSE](LICENSE).

 - image-text-to-text
 ---
+# Qwen3.8-Flash-Next DS4 Q2 (imatrix)
+The default Q2 release is now **44.81 GB (41.73 GiB)**, including MTP.
+It uses **IQ2_XXS gate/up experts and padded Q2_K down projections**, saving
+**5.16 GiB (11%)** over the previous MXFP4-down build with essentially
+unchanged measured speed. This is the smaller option for memory-constrained
+Macs, including 64 GB systems.
+The **external Q4_1 PLE sidecar is required** and reused from the
+[Q4 repository](https://huggingface.co/ivanfioravanti/Qwen3.8-Flash-Next-DS4-Q4).
+Both files together occupy **76.81 GB (71.53 GiB) on disk**. The PLE table is
+CPU-mapped and demand-paged; resident pages still consume RAM. Start with 8K
+context and a 1,024-token prefill chunk on a 64 GB Mac. Context buffers, host
+allocations, and other applications also affect memory use. The measurements
+below ran on an M3 Ultra with 512 GiB; they do not establish a physical 64 GB fit.
+## Model structure
+- **48 trunk layers, 512 routed experts per layer:** the 96 gate/up tensors
+  use imatrix-calibrated **IQ2_XXS**, unchanged from the previous release.
+- **48 trunk down tensors:** imatrix-calibrated **Q2_K**, quantized directly
+  from the original BF16 weights. Each 640-column logical input row is
+  zero-padded to **768 physical columns** for three 256-value Q2_K blocks.
+  Metal kernels use the padded weight stride and only the 640 real inputs;
+  the model's architecture and activation width remain unchanged.
+- **Dense GDN/QSA projections, shared experts and output:** **Q8_0**,
+  preserved from the previous model.
+- **Token embeddings:** **BF16**. Routers, norms, hyper-connections and
+  control tensors retain their existing F16/F32 precision.
+- **Embedded MTP block:** all 32 tensors preserved, including **Q4_K gate/up**
+  and **MXFP4 down** experts. Ordinary decoding and optional `--mtp`
+  speculation use the same GGUF.
+- **Required PLE n-gram sidecar:** **Q4_1**, approximately 32.00 GB / 29.80 GiB
+  on disk, shared with the Q4 release. It is not included in the main GGUF.
+Only 48 tensor payloads changed. The other **1,207 tensors are byte-identical**
+to the previous release, and all **1,255 output tensors** were read back and
+hash-verified. The `.gguf.json` file records output hashes, down-weight source
+hashes, calibration provenance, and the physical padding geometry.
+This is a DS4-targeted `qwen4exp` GGUF. It requires the padded-down runtime
+support in **commit `5bd8796` or later** on the
+[`qwen3.8-flash-next` branch](https://github.com/ivanfioravanti/ds4-metal/tree/qwen3.8-flash-next).
+Older builds reject the new down-tensor layout. Compatibility with other
+runners is not claimed.
+## Download and run
+Update the DS4 checkout to the current `qwen3.8-flash-next` branch, then build
+with `make -j8 ds4 ds4-server`. Run from that checkout's root:
+```sh
+./download_model.sh qwen38-q2
+./ds4 --metal \
+  --ple gguf/Qwen3.8-Flash-Next-PLE-Q4_1.gguf \
+  --ctx 8192 --prefill-chunk 1024
+```
+The script downloads this model and the PLE sidecar, then updates
+`ds4flash.gguf` to the new main model. For a custom directory, use:
 ```sh
+QWEN_Q2_DIR="$HOME/models/qwen38-ds4-iq2"
 hf download ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2 \
+  Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf \
+  --local-dir "$QWEN_Q2_DIR"
 hf download ivanfioravanti/Qwen3.8-Flash-Next-DS4-Q4 \
+  Qwen3.8-Flash-Next-PLE-Q4_1.gguf --local-dir "$QWEN_Q2_DIR"
 ./ds4 --metal \
+  -m "$QWEN_Q2_DIR/Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf" \
+  --ple "$QWEN_Q2_DIR/Qwen3.8-Flash-Next-PLE-Q4_1.gguf" \
+  --ctx 8192 --prefill-chunk 1024
 ```
+An existing Q4_1 PLE sidecar can be reused without downloading it again.
+Add `--mtp` for speculation; use `--mtp-exact-sampling` when preserving the
+target sampling distribution is required. The older main-model filename has
+been replaced, so update any explicit `-m` paths.
+For optional vision, run `./download_model.sh qwen38-vision` and add
+`--vision gguf/mmproj-Qwen3.8-Flash-Next-Q8_0.gguf`. The encoder is separate
+and unchanged. Earlier vision smoke scores belong to the superseded model;
+the tests below cover text, kernel correctness, and a short MTP check.
+## Measured tradeoff
+On **99 BF16-reference continuations / 2,376 tokens**, the old MXFP4-down
+model versus this Q2_K-down release measured:
+- BF16 top-token agreement: **91.33% → 90.32%**.
+- Target NLL: **0.290735 → 0.303570** (4.4% higher; lower is better).
+- First-token matches: **69/99 → 65/99**.
+On the matched **12-question hard-smoke suite**, with a 2,048-token answer
+budget, temperature 0, seed 123, 32K context and no retries or MTP:
+- Previous release: **6 correct, 0 wrong, 6 incomplete**.
+- Current release: **5 correct, 0 wrong, 7 incomplete**.
+Incomplete means no gradeable final answer. These limited tests show extra
+quantization drift and do not establish equal reasoning quality.
+Two benchmark passes in opposite model order used identical prompts,
+1,024-token prefill chunks and 128 teacher-forced decode tokens per frontier:
+- **4K:** decode **47.63 → 47.31 tok/s**; prefill **962.72 → 973.17 tok/s**.
+- **32K:** decode **47.09 → 46.94 tok/s**; prefill **926.06 → 935.95 tok/s**.
+The 32K prefill processes the additional 28,672 tokens after 4K. A separate
+128-token greedy smoke test produced identical output with and without MTP.
+Kernel tests cover padded decode and prefill, and the one-token CPU/GPU check
+agrees on top-1. See [full results](qwen38-q2down.md) and
+[machine-readable measurements](qwen38-q2down-results.json).
+## Provenance and integrity
+Source: [Qwen/Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next),
+BF16 revision `de4b8e4d43b917e7706784d8bb445c9af86a3540`.
+Calibration uses the pinned
+[Unsloth imatrix](https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF),
+revision `c8b5954a88c2775c546b92593eda40ea041d3176`, SHA-256
+`a5863123db1ca458727e738955bef7bfc199520aa2bee3a30142a1aff9254154`.
+It contains 45 chunks of 18,432 tokens. Sixteen gate/up and eight down entries
+with zero activation counts use the deterministic per-expert weight-energy
+fallback; the remaining entries use measured activation importance.
+Build tool and recipe:
+[`qwen4_iq2.py --projection down`](https://github.com/ivanfioravanti/ds4-metal/blob/5bd8796/gguf-tools/README.md#experimental-padded-q2_k-down-projections).
+Main GGUF SHA-256:
+`341c8d79468384a05e22998ae834a489145f6c385e276dcf79170a6b0d0ffd2d`.
+`SHA256SUMS` covers the published release files except itself and the Hub's
+`.gitattributes`. The previous weights and their text/vision reports remain
+available in [repository history](https://huggingface.co/ivanfioravanti/Qwen3.8-Flash-Next-DS4-IQ2/tree/672c52bbea7865352c8f0aa766c43939acf0f0d5).
+Derived from Qwen's original model, with calibration from Unsloth and the
+DS4 runtime/build tools. This derivative carries the **Qwen Community
+License 1.0**; the original copyright and full terms are in [LICENSE](LICENSE).

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+b4dd65841390a609f50b54cfdbf30db0eac21820e71d3c2792b0541f0e1f9b27  README.md
+c21f42f5a89c7fa4cbb8a4bb0b478546bbf2b5ecbd0de33ff07b4ab5401dea9a  qwen38-q2down-results.json
+807443bdd1fb3b373689954126bdb1fab228038bc85e8482851ea64668cbf13b  qwen38-q2down.md

qwen38-iq2-quality-results.json DELETED Viewed

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qwen38-iq2-quality.md DELETED Viewed

@@ -1,62 +0,0 @@
-# Qwen3.8 IQ2_XXS quality experiment
-IQ2_XXS is calibrated directly from the original BF16 trunk gate/up weights. All other tensors, including MXFP4 down projections and the MTP block, are copied from the existing Q4_K-imatrix combined GGUF and verified after writing. The required Q4_1 PLE sidecar remains external.
-Main model: **50.34 GB (46.89 GiB)**, including MTP. This is model storage, not the complete runtime memory budget.
-This is a promising candidate for a 64 GB memory budget: the hard-suite score remains comparable, but the BF16 probability comparison shows substantially more quantization drift than the four-bit recipes. Validate it on the target 64 GB machine and representative tasks before replacing a higher-precision build.
-## Hard evaluation
-All four recipes use the same frozen evaluator and Metal kernels on M3 Ultra, `--suite hard --ctx 32768 --temp 0 --seed 123`, with a 1024-token prefill chunk. Native per-case generation budgets and the evaluator’s thinking-close controller are retained. Speculation and retries are disabled. Each model is evaluated once on the same 50 cases.
-| Recipe | Passed / 50 | Failed | Incomplete |
-| --- | ---: | ---: | ---: |
-| q4k | 35 | 2 | 13 |
-| q40 | 35 | 0 | 15 |
-| q8 | 34 | 2 | 14 |
-| iq2 | 36 | 3 | 11 |
-Passes by source:
-| Source | Q4_K | Q4_0 | Q8 | IQ2 |
-| --- | ---: | ---: | ---: | ---: |
-| MMLU-Pro | 23/30 | 24/30 | 22/30 | 25/30 |
-| OlympiadBench | 2/10 | 2/10 | 2/10 | 2/10 |
-| LiveBench | 5/5 | 5/5 | 5/5 | 5/5 |
-| NIST Juliet | 5/5 | 4/5 | 5/5 | 4/5 |
-Paired differences count the exact questions on which the models disagree:
-| IQ2 versus | IQ2 alone passed | Baseline alone passed |
-| --- | ---: | ---: |
-| q4k | 2 | 1 |
-| q40 | 2 | 1 |
-| q8 | 3 | 1 |
-These small, fixed suites do not establish general quality equivalence. Q8 uses Q8 routed down weights as well as gate/up; Q4_0 is an earlier complete recipe. The most controlled quantization comparison is IQ2 versus Q4_K-imatrix.
-## BF16-reference continuations
-The existing exact-checkpoint fixture is rerun on all four builds. Fifteen cases with mismatched target-token counts are excluded consistently, leaving 85 aligned cases. This is a probability comparison on short continuations, not a long-context reasoning benchmark.
-| Recipe | Target NLL ↓ | First token / 85 | Top-1 agreement | Logprob MAE ↓ |
-| --- | ---: | ---: | ---: | ---: |
-| q4k | 0.21287 | 74 | 96.57% | 0.04307 |
-| q40 | 0.20959 | 73 | 96.52% | 0.04297 |
-| q8 | 0.19828 | 79 | 98.48% | 0.02070 |
-| iq2 | 0.27828 | 58 | 91.32% | 0.13992 |
-## Memory and provenance
-The 32K→64K ordinary-decode sweep allocates 65,793 context positions with a 1024-token prefill chunk. macOS `time -l` reports peak RSS **33.57 GB (31.27 GiB)** and peak footprint **3.54 GB (3.29 GiB)**. These process counters do not reliably account for all mmap-backed GPU weights and must not be interpreted as the total model memory requirement. A repeat of the same sweep with Metal allocation accounting reports **2.92 GiB** peak tracked runtime tensors. The main model file plus those tracked runtime allocations totals **53.48 GB**; this is a partial budget, excluding OS and driver overhead, other host allocations and resident pages of the external PLE sidecar. PLE pages are demand-paged and reclaimable, but their memory cost is not zero. The test machine has 512 GiB; this does not validate Metal allocation limits or OS headroom on a physical 64 GB Mac. The MTP smoke test is separate from this ordinary-decode memory measurement.
-Ordinary decoding measured 47.88 tokens/s at 32K and 47.49 tokens/s at 64K, each for 128 generated tokens. The sweep extends a shared prompt prefix from 32K to 64K; the second prefill measures the additional 32K tokens. The CSV’s generic `kvcache_bytes=0` field is not implemented for this architecture; the runtime allocation report above supplies the memory accounting.
-A separate ordinary/MTP pair uses identical greedy settings, a 256-token output cap and 64K allocated context. Generated stdout matches byte-for-byte: **True**. This is a short-prompt correctness smoke test, not a full-context MTP quality evaluation.
-The same pinned Unsloth imatrix as the Q4 build is used. Sixteen zero-count gate/up entries use the existing deterministic weight-energy fallback; calibration provenance and per-tensor source hashes are retained in the artifact manifest.
-Validation: `make -j8 ds4 ds4-eval`, evaluator case validation and extractor self-tests, 35 offline Qwen pack tests, and all 1,255 written tensor readback hashes passed. All four hard runs completed 50 cases; their nonzero grading exit codes reflect wrong/incomplete answers, not an execution failure. The supplementary MTP check accepted 120 drafts in 135 verification cycles (88.9%).
-[Machine-readable results](qwen38-iq2-quality-results.json) include per-case scores, commands, hashes, paired disagreements and calibration details. Full local traces and logits remain under the ignored `OUT/qwen38-iq2-quality` directory.

qwen38-q2down-results.json ADDED Viewed

	@@ -0,0 +1,645 @@

+{
+  "date": "2026-09-07",
+  "host": "MacStudioIvan",
+  "chip": "Apple M3 Ultra",
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+          "generated_tokens": "569",
+          "think_close": "natural"
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+          "id": "juliet-cwe121-array-index-01",
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+          "think_close": "natural"
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+      "tokens": 2048,
+      "context": 32768,
+      "prefill_chunk": 1024,
+      "temperature": 0,
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+      "metal/qwen4.metal": "210f4ffd614e3241931fb096f6e9b9c8b6090dfbb68d3aa50d9bfc1662fd2448"
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+    "final_validation_change": "A later host guard rejects invalid down activation widths not divisible by 32; measured valid 640-wide geometry is unaffected. Final sources will be rebuilt and kernel-tested."
+  },
+  "jobs": [
+    {
+      "name": "candidate-bf16-driver",
+      "command": [
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+        "run",
+        "--no-project",
+        "--with",
+        "numpy",
+        "--with",
+        "tokenizers",
+        "python",
+        "speed-bench/qwen38_q2down_compare.py",
+        "--model",
+        "/Users/ifioravanti/models/qwen38-q2down-experiment/Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf",
+        "--ple",
+        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
+        "--tokenizer",
+        "/Users/ifioravanti/.cache/huggingface/hub/models--Qwen--Qwen3.8-Flash-Next/snapshots/de4b8e4d43b917e7706784d8bb445c9af86a3540/tokenizer.json",
+        "--out",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/candidate-bf16"
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+      "started": 1788776877.7152152,
+      "returncode": 0,
+      "elapsed_seconds": 68.86908960342407
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+    {
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+      "command": [
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+        "-m",
+        "/Users/ifioravanti/models/Qwen3.8-Flash-Next-DS4-IQ2/Qwen3.8-Flash-Next-IQ2XXSImatrix-MXFP4Down-MTP.gguf",
+        "--ple",
+        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
+        "--metal",
+        "--prompt-file",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/bench-prompt.txt",
+        "--ctx-start",
+        "4096",
+        "--ctx-max",
+        "32768",
+        "--step-mul",
+        "8",
+        "--ctx-alloc",
+        "33025",
+        "--gen-tokens",
+        "128",
+        "--teacher-forced-decode",
+        "--prefill-chunk",
+        "1024",
+        "--csv",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/baseline-bench-1.csv"
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+      "returncode": 1,
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+        "--ple",
+        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
+        "--metal",
+        "--prompt-file",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/bench-prompt.txt",
+        "--ctx-start",
+        "4096",
+        "--ctx-max",
+        "32768",
+        "--step-mul",
+        "8",
+        "--ctx-alloc",
+        "33025",
+        "--gen-tokens",
+        "128",
+        "--teacher-forced-decode",
+        "--prefill-chunk",
+        "1024",
+        "--csv",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/baseline-bench-1.csv"
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+    {
+      "name": "candidate-bench-1",
+      "command": [
+        "./ds4-bench",
+        "-m",
+        "/Users/ifioravanti/models/qwen38-q2down-experiment/Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf",
+        "--ple",
+        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
+        "--metal",
+        "--prompt-file",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/bench-prompt.txt",
+        "--ctx-start",
+        "4096",
+        "--ctx-max",
+        "32768",
+        "--step-mul",
+        "8",
+        "--ctx-alloc",
+        "33025",
+        "--gen-tokens",
+        "128",
+        "--teacher-forced-decode",
+        "--prefill-chunk",
+        "1024",
+        "--csv",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/candidate-bench-1.csv"
+      ],
+      "started": 1788777023.555121,
+      "returncode": 0,
+      "elapsed_seconds": 41.66611909866333
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+    {
+      "name": "candidate-bench-2",
+      "command": [
+        "./ds4-bench",
+        "-m",
+        "/Users/ifioravanti/models/qwen38-q2down-experiment/Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf",
+        "--ple",
+        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
+        "--metal",
+        "--prompt-file",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/bench-prompt.txt",
+        "--ctx-start",
+        "4096",
+        "--ctx-max",
+        "32768",
+        "--step-mul",
+        "8",
+        "--ctx-alloc",
+        "33025",
+        "--gen-tokens",
+        "128",
+        "--teacher-forced-decode",
+        "--prefill-chunk",
+        "1024",
+        "--csv",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/candidate-bench-2.csv"
+      ],
+      "started": 1788777065.2217221,
+      "returncode": 0,
+      "elapsed_seconds": 41.78662300109863
+    },
+    {
+      "name": "baseline-bench-2",
+      "command": [
+        "./ds4-bench",
+        "-m",
+        "/Users/ifioravanti/models/Qwen3.8-Flash-Next-DS4-IQ2/Qwen3.8-Flash-Next-IQ2XXSImatrix-MXFP4Down-MTP.gguf",
+        "--ple",
+        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
+        "--metal",
+        "--prompt-file",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/bench-prompt.txt",
+        "--ctx-start",
+        "4096",
+        "--ctx-max",
+        "32768",
+        "--step-mul",
+        "8",
+        "--ctx-alloc",
+        "33025",
+        "--gen-tokens",
+        "128",
+        "--teacher-forced-decode",
+        "--prefill-chunk",
+        "1024",
+        "--csv",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/baseline-bench-2.csv"
+      ],
+      "started": 1788777107.008838,
+      "returncode": 0,
+      "elapsed_seconds": 42.11957907676697
+    },
+    {
+      "name": "candidate-hard-smoke",
+      "command": [
+        "./ds4-eval",
+        "-m",
+        "/Users/ifioravanti/models/qwen38-q2down-experiment/Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf",
+        "--ple",
+        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
+        "--metal",
+        "--suite",
+        "hard-smoke",
+        "--ctx",
+        "32768",
+        "--prefill-chunk",
+        "1024",
+        "--temp",
+        "0",
+        "--seed",
+        "123",
+        "--tokens",
+        "2048",
+        "--plain",
+        "--pause-ms",
+        "1",
+        "--trace",
+        "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down/candidate-hard-smoke.trace"
+      ],
+      "started": 1788777149.1288981,
+      "returncode": 1,
+      "elapsed_seconds": 390.98224997520447
+    },
+    {
+      "name": "candidate-cpu-gpu",
+      "command": [
+        "./ds4",
+        "-m",
+        "/Users/ifioravanti/models/qwen38-q2down-experiment/Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf",
+        "--ple",
+        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
+        "--metal",
+        "--ctx",
+        "4096",
+        "--first-token-test",
+        "--raw",
+        "-p",
+        "x"
+      ],
+      "started": 1788777540.111547,
+      "returncode": 0,
+      "elapsed_seconds": 4.942179918289185
+    },
+    {
+      "name": "candidate-greedy",
+      "command": [
+        "./ds4",
+        "-m",
+        "/Users/ifioravanti/models/qwen38-q2down-experiment/Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf",
+        "--ple",
+        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
+        "--metal",
+        "--ctx",
+        "4096",
+        "--temp",
+        "0",
+        "--seed",
+        "123",
+        "-n",
+        "128",
+        "-p",
+        "Write a Python function that returns the prime factors of a positive integer.",
+        "--nothink",
+        "--prefill-chunk",
+        "1024"
+      ],
+      "started": 1788777545.054121,
+      "returncode": 0,
+      "elapsed_seconds": 4.287975788116455
+    },
+    {
+      "name": "candidate-mtp",
+      "command": [
+        "./ds4",
+        "-m",
+        "/Users/ifioravanti/models/qwen38-q2down-experiment/Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf",
+        "--ple",
+        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
+        "--metal",
+        "--ctx",
+        "4096",
+        "--temp",
+        "0",
+        "--seed",
+        "123",
+        "-n",
+        "128",
+        "-p",
+        "Write a Python function that returns the prime factors of a positive integer.",
+        "--nothink",
+        "--prefill-chunk",
+        "1024",
+        "--mtp"
+      ],
+      "started": 1788777549.342518,
+      "returncode": 0,
+      "elapsed_seconds": 3.3929851055145264
+    }
+  ],
+  "validation": {
+    "tensor_readback_hashes": 1255,
+    "unchanged_payload_hashes": 1207,
+    "pack_tests_passed": 35,
+    "metal_kernel_suite": "passed",
+    "cpu_gpu_one_token_max_logit_error": 0.00147057,
+    "cpu_gpu_one_token_top1_equal": true,
+    "final_binary_greedy_smoke_equal": true
+  },
+  "final_build_sha256": {
+    "ds4": "d127bd8bee9625430f472d65ce965544cd43cfde9b4f28e5c78fab2dfd99e8f5",
+    "ds4-eval": "6ebdcb096fadcef05d4fba7edc2d48c63f20270d4e1f9c1812802f9ad3807ff1",
+    "ds4-bench": "1db4fc9d2403f3ec3d98bc4a1bcbcecdabd5a469db14464f5804601a55480cff",
+    "ds4.c": "ae40992d0777a80e0a30582b7dda66099f8f0284967b46d21a8d12d8991436cb",
+    "ds4_metal.m": "b9a2ffe1fe271f86c48433ca2d3c640f0fe971d5f7ca5748bb53b6a24931feb8",
+    "metal/qwen4.metal": "210f4ffd614e3241931fb096f6e9b9c8b6090dfbb68d3aa50d9bfc1662fd2448",
+    "gguf-tools/qwen4_iq2.py": "c590438d13f35ac884ba9f6e187d264bfa8b5195edf917b675ebfadd87fbe327"
+  },
+  "raw_artifacts": "/Users/ifioravanti/github/ds4/OUT/qwen38-q2down"
+}

qwen38-q2down.md ADDED Viewed

	@@ -0,0 +1,93 @@

+# Qwen3.8 padded Q2_K down experiment
+The candidate saves **5.16 GiB (11%)** with essentially unchanged inference
+speed on MacStudioIvan (M3 Ultra, 512 GiB RAM). It shows modest additional
+probability drift and one fewer completed correct answer in the capped smoke
+suite. This experiment measures the smaller model now used for the default Q2 release.
+## Artifact
+- Original main GGUF: **50,343,093,376 bytes / 46.89 GiB**.
+- Candidate main GGUF: **44,806,612,192 bytes / 41.73 GiB**.
+- Saved: **5,536,481,184 bytes** (metadata adds 96 bytes).
+- Only the 48 trunk expert down tensors changed, from MXFP4 to calibrated
+  Q2_K. All 1,207 other tensor payload hashes match the original manifest.
+- Quantization uses original BF16 weights and the pinned Unsloth imatrix.
+  Eight zero-count down entries use the existing per-expert weight-energy
+  fallback. Gate/up IQ2_XXS weights and the complete MTP block are unchanged.
+- Down rows store 768 physical columns for 640 logical activations: three
+  84-byte Q2_K blocks replace twenty 17-byte MXFP4 blocks per row.
+- The required external Q4_1 PLE sidecar is unchanged and excluded from these
+  sizes. These tests do not establish a fit on a physical 64 GB machine.
+Candidate SHA-256:
+`341c8d79468384a05e22998ae834a489145f6c385e276dcf79170a6b0d0ffd2d`.
+## Quality
+On 99 saved BF16-reference continuations (2,376 tokens), using identical
+prompt/target token IDs and the same executable for both models:
+- BF16 top-token agreement: **91.33% → 90.32%** (down 1.01 percentage points).
+- Target NLL: **0.290735 → 0.303570** (up 4.4%; lower is better).
+- Target logprob MAE: **0.161377 → 0.177123**.
+- First-token matches: **69/99 → 65/99**.
+- Per-case NLL favors the original on 66 cases and the candidate on 33.
+The fixture's original tokenizer encodes prompt segments and continuations
+directly into the production Metal teacher-forcing path. Case 088 is excluded
+because token boundaries do not round-trip to the saved reference bytes.
+This differs from the earlier 85-case scoring pipeline; compare models within
+this experiment. Short continuations do not establish reasoning equivalence.
+The 12-question `hard-smoke` suite uses 32K context, 1,024-token prefill chunks,
+temperature 0, seed 123, a 2,048-token generation cap, and the standard
+thinking-close controller. No retries or MTP:
+- Original: **6 passed, 0 wrong, 6 incomplete**.
+- Candidate: **5 passed, 0 wrong, 7 incomplete**.
+- MMLU-Pro case 6500 changes from passed to incomplete; other statuses match.
+  Incomplete means no gradeable final answer, not necessarily an exhausted
+  budget. The candidate's final security case ended before the cap.
+## Speed
+Two sequential passes in opposite model order, identical raw prompt tokens,
+128 teacher-forced decode tokens per frontier, 33,025 allocated positions,
+and 1,024-token prefill chunks. Arithmetic means:
+- **4K:** decode **47.63 → 47.31 tok/s**; prefill **962.72 → 973.17 tok/s**.
+- **32K:** decode **47.09 → 46.94 tok/s**; prefill **926.06 → 935.95 tok/s**.
+The 32K prefill measurement processes the additional 28,672 tokens after 4K.
+Decode differences are under 1% and comparable to run variation. The generic
+CSV `kvcache_bytes=0` at 32K is not a total-memory measurement.
+A separate short-prompt, 128-token greedy smoke test produced identical text
+with MTP enabled and disabled. Generation measured 73.89 versus 50.97 tok/s
+respectively in that single pair; this is not a general MTP speed benchmark.
+## Verification and use
+All 1,255 output tensor payloads were read back and hash-verified. Physical
+down dimensions and the unchanged MTP layout were independently checked.
+The complete Qwen Metal kernel suite passes, including padded Q2_K decode,
+tiled prefill, shared experts, deliberately nonzero padding, an aligned Q2_K
+control, and rejection of invalid activation widths. All 35 pack tests pass.
+The candidate's one-token CPU/GPU comparison agrees on top-1, with maximum
+logit difference 0.00147057. The final rebuilt binary also reproduces the
+candidate's greedy smoke output.
+Use the rebuilt executable from this checkout; older builds reject the padded
+layout. The original model and default model selection were preserved.
+```sh
+./ds4 --metal \
+  -m "$HOME/models/qwen38-q2down-experiment/Qwen3.8-Flash-Next-IQ2XXSImatrix-Q2KDownPad768-MTP.gguf" \
+  --ple "$HOME/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf" \
+  --ctx 32768 --prefill-chunk 1024
+```
+Add `--mtp` for speculation. See [build instructions](https://github.com/ivanfioravanti/ds4-metal/blob/5bd8796/gguf-tools/README.md#experimental-padded-q2_k-down-projections)
+and [machine-readable results](qwen38-q2down-results.json). Raw logits, traces,
+commands and measured binaries are under the ignored `OUT/qwen38-q2down/`.

qwen38-vision-quality-results.json DELETED Viewed

@@ -1,176 +0,0 @@
-{
-  "revision": "3bf8552ee49a4290126be9d503e72f96bff494bc",
-  "encoder": "/Users/ifioravanti/github/ds4/gguf/mmproj-Qwen3.8-Flash-Next-Q8_0.gguf",
-  "encoder_sha256": "b2e9b5e4a44c107f8867e67dbf09b607fd99ae33c1a97a60a6720aeb252a9dad",
-  "runs": [
-    {
-      "model": "iq2",
-      "command": [
-        "/Users/ifioravanti/github/ds4/ds4-server",
-        "-m",
-        "/Users/ifioravanti/models/qwen38-iq2-imatrix/Qwen3.8-Flash-Next-IQ2XXSImatrix-MXFP4Down-MTP.gguf",
-        "--ple",
-        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
-        "--vision",
-        "/Users/ifioravanti/github/ds4/gguf/mmproj-Qwen3.8-Flash-Next-Q8_0.gguf",
-        "--metal",
-        "--ctx",
-        "8192",
-        "--prefill-chunk",
-        "1024",
-        "--host",
-        "127.0.0.1",
-        "--port",
-        "18938"
-      ],
-      "started": 1788733344.69397,
-      "returncode": 0,
-      "cases": [
-        {
-          "name": "diagram",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "Based on the diagram provided:\n\nThe three box labels from left to right are:\n1.  **INPUT**\n2.  **ENCODER**\n3.  **SUMMARY**\n\nThe box that immediately follows ENCODER is **SUMMARY**."
-        },
-        {
-          "name": "ocr",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "Venue: NORTH HARBOR\nTrain: 482\nGate: C7\nDeparture: 19:45\nSeat: 14A\nAccess code: MINT-731"
-        },
-        {
-          "name": "spatial",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "Based on the provided image, here are the three colored shapes and their relative positions:\n\n1.  **Blue Circle**\n    *   Located in the **upper-left** portion of the image.\n    *   It is to the **left** of the Red Triangle.\n    *   It is **above** and slightly to the **left** of the Green Square.\n\n2.  **Red Triangle**\n    *   Located in the **upper-right** portion of the image.\n    *   It is to the **right** of the Blue Circle.\n    *   It is **above** and slightly to the **right** of the Green Square.\n\n3.  **Green Square**\n    *   Located in the **lower-center** portion of the image.\n    *   It is **below** both the Blue Circle and the Red Triangle.\n    *   It is positioned **between** the Blue Circle (to its upper-left) and the Red Triangle (to its upper-right).\n\nIn summary:\n- The Blue Circle and Red Triangle are positioned side-by-side at the top.\n- The Green Square is centered below them, forming a triangular arrangement with the other two shapes."
-        },
-        {
-          "name": "screenshot",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "Based on the screenshot:\n\n- **Selected file:** `deploy.sh` (highlighted in yellow/orange in the FILES sidebar)\n- **Build status:** FAILED (shown in red text: \"Build 42: FAILED\")\n- **Exact error:** `error: missing CUDA_HOME`\n- **Exit status:** `exit status 2`"
-        },
-        {
-          "name": "photo",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "The main subject of this photograph is **Planet Earth**, viewed from space. It is a full-globe image, often referred to as the \u201cBlue Marble,\u201d showcasing the planet\u2019s oceans, continents, and swirling cloud patterns against the blackness of space.\n\nThe most clearly visible continent in this image is **Africa**. Its distinctive shape \u2014 including the Horn of Africa, the Sahel region, and the southern tip \u2014 is prominently displayed in the center-left portion of the globe.\n\nThe large island off its southeastern coast is **Madagascar**. It is clearly visible as a long, narrow landmass situated in the Indian Ocean, separated from the African mainland by the Mozambique Channel.\n\n---\n\n**Summary:**\n- **Main Subject**: Planet Earth (viewed from space)\n- **Most Clearly Visible Continent**: Africa\n- **Large Island Off Southeastern Coast**: Madagascar\n\nThis iconic view was famously captured by the Apollo 17 crew in 1972, though similar images have been taken by satellites since then. The clarity of Africa and Madagascar makes them easily identifiable landmarks in this global perspective."
-        },
-        {
-          "name": "unrelated",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "No. The image shows three labeled geometric shapes: a blue circle, a red triangle, and a green square."
-        }
-      ],
-      "passed": 6,
-      "finished": 1788733371.069199
-    },
-    {
-      "model": "q4k",
-      "command": [
-        "/Users/ifioravanti/github/ds4/ds4-server",
-        "-m",
-        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-Q4KImatrix-MTP-qwen4exp-pleext.gguf",
-        "--ple",
-        "/Users/ifioravanti/models/qwen38-q4k-imatrix/Qwen3.8-Flash-Next-PLE-Q4_1.gguf",
-        "--vision",
-        "/Users/ifioravanti/github/ds4/gguf/mmproj-Qwen3.8-Flash-Next-Q8_0.gguf",
-        "--metal",
-        "--ctx",
-        "8192",
-        "--prefill-chunk",
-        "1024",
-        "--host",
-        "127.0.0.1",
-        "--port",
-        "18938"
-      ],
-      "started": 1788733371.069612,
-      "returncode": 0,
-      "cases": [
-        {
-          "name": "diagram",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "Based on the provided diagram, here is the information you requested:\n\n-   The three box labels from left to right are: **INPUT**, **ENCODER**, and **SUMMARY**.\n-   The box that immediately follows ENCODER is **SUMMARY**."
-        },
-        {
-          "name": "ocr",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "Based on the image provided, here are the transcribed details:\n\n*   **Venue:** NORTH HARBOR\n*   **Train:** 482\n*   **Gate:** C7\n*   **Departure:** 19:45\n*   **Seat:** 14A\n*   **Access Code:** MINT-731"
-        },
-        {
-          "name": "spatial",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "Based on the image provided, here are the three colored shapes and their relative positions:\n\n1.  **Blue Circle**: Located in the **top-left** portion of the image.\n2.  **Red Triangle**: Located in the **top-right** portion of the image.\n3.  **Green Square**: Located in the **bottom-center** portion of the image.\n\n---\n\n### Relative Positions:\n\n- The **Blue Circle** is to the **left** of the Red Triangle and **above** the Green Square.\n- The **Red Triangle** is to the **right** of the Blue Circle and **above** the Green Square.\n- The **Green Square** is **below** both the Blue Circle and the Red Triangle, and centered horizontally between them.\n\nIn summary:\n- Top row: Blue Circle (left), Red Triangle (right)\n- Bottom row: Green Square (centered below the top two)"
-        },
-        {
-          "name": "screenshot",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "Based on the screenshot provided:\n\n1.  **Selected File:** The file `deploy.sh` is selected (highlighted in yellow in the left sidebar).\n2.  **Build Status:** The build **failed** (indicated by the red text \"Build 42: FAILED\" in the terminal).\n3.  **Exact Error:** The error message shown is `error: missing CUDA_HOME`.\n4.  **Exit Status:** The exit status displayed is `exit status 2`."
-        },
-        {
-          "name": "photo",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "The main subject of this photograph is the planet **Earth**, viewed from space. The image prominently features the African continent, with its distinctive shape and reddish-brown terrain clearly visible against the blue oceans and swirling white clouds.\n\nThe most clearly visible continent is **Africa**.\n\nThe large island off its southeastern coast is **Madagascar**. It is easily identifiable as the elongated landmass situated in the Indian Ocean, separated from the mainland by the Mozambique Channel."
-        },
-        {
-          "name": "unrelated",
-          "pass": true,
-          "missing": [],
-          "forbidden": [],
-          "provider": null,
-          "answer": "No. The image shows three labeled geometric shapes: a blue circle, a red triangle, and a green square."
-        }
-      ],
-      "passed": 6,
-      "finished": 1788733414.885879
-    }
-  ],
-  "file_sha256": {
-    "ds4-server": "d5fd5dfae1a716dd10e2057af6fc90d75268faa28836c72e3aff2540a58e9580",
-    "tests/run_glm53_vision_quality.py": "849a97fe0f1ae37278f9306a64759baf5b6b114d9457905e3dc355fcbb13723b",
-    "tests/vision-fixtures/glm53/README.md": "7ba16d74fa2b5c56b12ae30587e8492128a39ada615ee7de885ba89ecccda9a8",
-    "tests/vision-fixtures/glm53/cases.json": "b95deec8bde6640a4828b76391f656f40e8eac098f0dff5396bda3f3df10bcea",
-    "tests/vision-fixtures/glm53/diagram.png": "ed423c191b553ff2e6fea5c01e0df7d0f8766d254a2df00b0d8b8d32ed54be45",
-    "tests/vision-fixtures/glm53/earth.jpg": "a48278513a38768ff92247972e60bc873eb361a8527cb8686a3e19c3ae1bdf9d",
-    "tests/vision-fixtures/glm53/screenshot.png": "28d2f8b7860e520df26542041e263c9cf641e9c5bff0652e67b186e55a6b073c",
-    "tests/vision-fixtures/glm53/spatial.png": "515542d4bc1bc9b1922ed417757eed225be4c1486fdc292ad373ebf24f0e42f9",
-    "tests/vision-fixtures/glm53/text.png": "3527da96122fd75d3b7cff45cebf8ad0ce27b5971d9b460d62e2ceb59b833921"
-  },
-  "settings": {
-    "temperature": 0,
-    "reasoning_effort": "none",
-    "max_tokens": 320,
-    "max_image_tokens": 1024,
-    "mtp": false,
-    "retries": 0
-  }
-}

qwen38-vision-quality.md DELETED Viewed

@@ -1,52 +0,0 @@
-# Qwen3.8 IQ2 and Q4_K vision smoke test
-Both recipes passed all six existing API vision fixtures on the M3 Ultra
-512 GiB machine. Tested revision: `3bf8552`, September 7, 2026 (Europe/Rome).
-| Case | IQ2_XXS imatrix | Q4_K imatrix |
-| --- | --- | --- |
-| Diagram labels and order | Pass | Pass |
-| Exact OCR | Pass | Pass |
-| Spatial relationships | Pass | Pass |
-| Software screenshot | Pass | Pass |
-| Earth photograph | Pass | Pass |
-| Rejection of unrelated train-ticket premise | Pass | Pass |
-The IQ2 recipe is the 50.34 GB mixed IQ2_XXS gate/up, MXFP4 down build from
-the [text quality comparison](qwen38-iq2-quality.md). The Q4_K recipe is its
-combined MTP template. Both use the same external Q4_1 PLE and downloaded
-`mmproj-Qwen3.8-Flash-Next-Q8_0.gguf` vision encoder (616,703,104 bytes).
-Servers ran serially on Metal, with 8,192 context positions, a 1,024-token
-prefill chunk, a maximum of 1,024 image tokens, and MTP disabled. The existing
-`tests/run_glm53_vision_quality.py` runner sent local OpenAI-compatible API
-requests with temperature 0, `reasoning_effort=none`, and a 320-token output
-limit. No retries or fixture changes were made. Both runner exits were zero;
-both temporary servers were stopped after testing.
-`make -j8 ds4-server` succeeded. To reproduce, launch one model at a time:
-```sh
-./ds4-server -m /path/to/model.gguf --ple /path/to/PLE-Q4_1.gguf \
-  --vision gguf/mmproj-Qwen3.8-Flash-Next-Q8_0.gguf \
-  --metal --ctx 8192 --prefill-chunk 1024 --host 127.0.0.1 --port 18938
-```
-In another terminal:
-```sh
-DS4_VISION_ENDPOINT=http://127.0.0.1:18938/v1/chat/completions \
-DS4_VISION_MODEL=qwen3.8-flash-next DS4_VISION_SUITE=qwen38 \
-DS4_VISION_REASONING_EFFORT=none \
-python3 tests/run_glm53_vision_quality.py
-```
-This is a small functional smoke suite using required/forbidden answer
-substrings. It confirms basic image processing and response quality, but does
-not establish general vision accuracy, IQ2/Q4_K equivalence, or numerical
-agreement of the encoder with the original HF checkpoint. The separate
-`make test-qwen4-vision` reference comparison was not run.
-[Full answers and provenance](qwen38-vision-quality-results.json) include the
-exact server commands, encoder hash, fixture hashes and per-case results.
-Raw server logs remain locally under `OUT/qwen38-vision-quality/`.