Instructions to use Accio-Lab/occamy-1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Accio-Lab/occamy-1.0-GGUF 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 Accio-Lab/occamy-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Accio-Lab/occamy-1.0-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Accio-Lab/occamy-1.0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Accio-Lab/occamy-1.0-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Accio-Lab/occamy-1.0-GGUF",
		"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/Accio-Lab/occamy-1.0-GGUF:Q4_K_M

Ollama
How to use Accio-Lab/occamy-1.0-GGUF with Ollama:
```
ollama run hf.co/Accio-Lab/occamy-1.0-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Accio-Lab/occamy-1.0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M

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": "Accio-Lab/occamy-1.0-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Accio-Lab/occamy-1.0-GGUF with Docker Model Runner:
```
docker model run hf.co/Accio-Lab/occamy-1.0-GGUF:Q4_K_M
```

Lemonade

How to use Accio-Lab/occamy-1.0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Accio-Lab/occamy-1.0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.occamy-1.0-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Accio-Lab/occamy-1.0-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M

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 Accio-Lab/occamy-1.0-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Accio-Lab/occamy-1.0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M

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 "Accio-Lab/occamy-1.0-GGUF:Q4_K_M" \
  --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"

occamy-1.0-GGUF / VALIDATION.md

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Add ten Occamy GGUF quantizations and validation results

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# Occamy GGUF validation

**Benchmark scope:** These measurements use the original qwen35-tagged artifacts. The published copies have qwen2 metadata with identical tensor payloads and separately passed bounded tokenizer and load checks; see [release verification](validation/release.json). Full benchmarks were not repeated after the metadata correction.

This report covers **12 quantization types and a BF16 reference**. It uses existing measurements only. The 60-case screen per artifact contains 24 GSM8K questions, 24 HumanEval+ tasks, 6 strict JSON cases and 6 native-tool cases. **All 312 code outputs were actually executed** with the pinned base and extended tests in an isolated CPU sandbox. This is a small fixed screen, not a full official benchmark.

[Machine-readable results](validation/results.json) · [780 per-question results](validation/per_question.jsonl)

Source: [Accio-Lab/occamy-1.0 at 8f8e0e5](https://huggingface.co/Accio-Lab/occamy-1.0/tree/8f8e0e58a3c9df042be1a3fa2c191fd8047acfb8). Runtime: [llama.cpp at 972d2313](https://github.com/ggml-org/llama.cpp/tree/972d2313bc0bf0a45f634f77d95c9fb03aeab12c). Hardware: NVIDIA B200; one GPU per job; no MTP.

## Quality results

| Artifact | PPL | Δ vs direct BF16 | Math flexible | Math strict | Code base+extended | Strict JSON | Native tools | Truncated / 60 |
|---|---:|---:|---:|---:|---:|---:|---:|---:|
| BF16 | 6.238500 | 0.000000 | 18/24 | 0/24 | 23/24 | 4/6 | 6/6 | 2 |
| IQ2_M | 7.444683 | 1.206183 | 19/24 | 0/24 | 22/24 | 4/6 | 6/6 | 2 |
| Q2_K | 7.418311 | 1.179811 | 19/24 | 0/24 | 22/24 | 4/6 | 6/6 | 3 |
| IQ3_M | 6.565706 | 0.327206 | 21/24 | 0/24 | 22/24 | 4/6 | 6/6 | 0 |
| Q3_K_M | 6.740173 | 0.501673 | 21/24 | 0/24 | 22/24 | 4/6 | 6/6 | 0 |
| IQ4_XS | 6.314710 | 0.076210 | 19/24 | 0/24 | 23/24 | 4/6 | 6/6 | 1 |
| Q4_K_M | 6.242854 | 0.004354 | 19/24 | 0/24 | 24/24 | 4/6 | 6/6 | 1 |
| Q5_K_M | 6.248758 | 0.010258 | 19/24 | 0/24 | 23/24 | 4/6 | 6/6 | 1 |
| Q6_K | 6.259335 | 0.020835 | 18/24 | 0/24 | 24/24 | 4/6 | 6/6 | 1 |
| Q8_0 | 6.257434 | 0.018934 | 18/24 | 0/24 | 23/24 | 4/6 | 6/6 | 2 |
| IQ3_XS | 6.597194 | 0.358694 | 19/24 | 0/24 | 24/24 | 4/6 | 6/6 | 3 |
| IQ4_NL | 6.318163 | 0.079663 | 19/24 | 0/24 | 23/24 | 4/6 | 6/6 | 3 |
| Q4_K_S | 6.282276 | 0.043776 | 21/24 | 0/24 | 23/24 | 4/6 | 6/6 | 0 |

Math strict-match uses the unchanged required-format extractor. The prompt did not request `####`; its failures must not be read as zero mathematical ability. Flexible-extract is reported separately using the upstream extractor. JSON is parsed as a complete response; fenced JSON fails strict parsing. Truncation remains part of the denominator. Per-question JSONL contains the original assistant content and tool_calls exactly as stored, alongside outcomes, token usage, truncation, code-test status and response digests. Dataset/task IDs identify prompts; server and infrastructure metadata are excluded.
PPL uses 32 × 512-token chunks and 8,160 scored tokens from a fixed Wikitext-2 test subset. Direct BF16 PPL is 6.2385, rounded to four decimals. The paired dump contains **lossy uint16 log-probabilities**: BF16 self-replay 6.238510 versus decoded dump base 6.232933, a 0.005577 floor. Native paired deltas and KL are kept separately in JSON.
Higher PPL in low-bit variants reflects a measured quality tradeoff, not detected artifact damage. All ten newly quantized artifacts passed full dequantized finite scans of all 733 tensors, and all twelve quantized artifacts were load-tested and benchmarked. Finite/load checks do not guarantee BF16-equivalent answers.
## Engine throughput
Native warmup and five repetitions; values are mean ± standard deviation in tokens/s. Eight CPU threads, full GPU offload requested, flash attention, F16 K/V cache, batch 2048 and microbatch 512. Tokenization and sampling are excluded. End-to-end latency and first-token latency for six prompts × three repetitions are available in results.json.
| Artifact | PP512 | TG128 @512 | PP2048 | TG128 @2048 |
|---|---:|---:|---:|---:|
| BF16 | 2959.44 ± 74.64 | 192.96 ± 3.48 | 3009.48 ± 17.28 | 193.15 ± 2.79 |
| IQ2_M | 5495.64 ± 980.55 | 211.60 ± 8.84 | 5814.88 ± 229.21 | 204.74 ± 20.24 |
| Q2_K | 4806.40 ± 994.10 | 233.02 ± 6.45 | 5085.95 ± 122.89 | 227.00 ± 18.29 |
| IQ3_M | 6262.15 ± 829.11 | 206.39 ± 5.20 | 6522.41 ± 183.47 | 203.86 ± 9.48 |
| Q3_K_M | 5998.35 ± 807.62 | 232.86 ± 2.80 | 5773.11 ± 897.22 | 232.02 ± 3.48 |
| IQ4_XS | 7037.35 ± 984.59 | 227.52 ± 10.09 | 7141.30 ± 228.24 | 228.19 ± 6.50 |
| Q4_K_M | 6346.99 ± 1065.66 | 241.55 ± 3.04 | 6311.66 ± 552.28 | 238.52 ± 7.65 |
| Q5_K_M | 6388.11 ± 711.30 | 237.84 ± 9.99 | 6310.46 ± 326.16 | 238.31 ± 9.63 |
| Q6_K | 5759.14 ± 824.86 | 223.94 ± 4.21 | 5765.40 ± 418.06 | 221.35 ± 9.85 |
| Q8_0 | 6719.33 ± 1768.73 | 223.50 ± 4.42 | 7072.93 ± 340.43 | 223.21 ± 4.71 |
| IQ3_XS | 6166.29 ± 1164.82 | 203.79 ± 2.58 | 6518.86 ± 339.58 | 203.77 ± 3.59 |
| IQ4_NL | 6856.12 ± 1322.34 | 241.74 ± 12.60 | 6910.83 ± 666.88 | 244.75 ± 3.58 |
| Q4_K_S | 6305.76 ± 1593.44 | 239.67 ± 4.07 | 6584.67 ± 270.46 | 237.58 ± 6.62 |
**Comparison limits:** IQ3_XS, IQ4_NL and Q4_K_S used a shared queue across four B200 cards, with each job on one card. Different cards and simultaneous CPU/IO activity introduce confounding relative to the earlier single-card measurements. Results are screening evidence, not a controlled performance ranking.
Ten new quantizations share one BF16 input and imatrix. The reused Q4_K_M and Q8_0 artifacts have different historical conversion recipes. Differences therefore cannot be attributed to bit width alone.

**Tokenizer boundary:** Benchmark requests use qwen35, aligned to the GGUF BF16 reference. This does not mean they reproduce the HF source tokenizer. Separate small probes found the source uses NFC normalization plus qwen2 behavior and showed disagreement with qwen35. Reused Q4/Q8 receive a qwen35 runtime override without changing their artifact bytes. Existing scores are retained as measurements under that configuration; the correction and its limited validation are documented in [tokenizer review](TOKENIZER.md). Existing Q4_K_M/Q8_0 files remain unchanged; BF16 is not a publication candidate.
## Exact measured artifacts (before release metadata correction)
The SHA256 values below identify the original benchmark inputs. Published-file SHA256 values are in [SHA256SUMS-new](SHA256SUMS-new) and [release mapping](validation/release.json). Sizes are file bytes, not runtime memory. BF16 is listed as the measured reference; inclusion in this report does not imply it is uploaded with the quantized artifacts.
| Artifact filename | Bytes | SHA256 |
|---|---:|---|
| occamy-1.0-BF16.gguf | 69376636992 | bbad6c5aaf28f20a700748613f998a2c0a98b6df94766ec37bf5c4a2cbf2aadf |
| occamy-1.0-IQ2_M.gguf | 11659235328 | 93a72b1723a06b5572d6fe9bafec42dfac1ef8b93cb1f0bf7389143b085ea8d8 |
| occamy-1.0-Q2_K.gguf | 12939593728 | caed9d813887780c3b614bec677b46e81acefdf8b13a0cfd6877df4b48ca79f4 |
| occamy-1.0-IQ3_M.gguf | 15440519168 | dd0ecc62fc13004e3c1c6094559a8d45387eca8d0b843b8c3e971eca1ed479ce |
| occamy-1.0-Q3_K_M.gguf | 16764764160 | a41137554d3d58e56dc2c043650ff92dbaf12822eb8be4a7a0d05ee1e9717d0c |
| occamy-1.0-IQ4_XS.gguf | 18728777728 | c37131d9049f031f209f2190bccb482a1d75ad9f2699ef86b67d1a193dbf1eae |
| occamy-1.0-Q4_K_M.gguf | 21166757696 | ffb25f763ff9c27f5f4e2adcdef399c5654f9f840fdac33dffe7035ba8266a87 |
| occamy-1.0-Q5_K_M.gguf | 24729131008 | 43cb6af4a64e846a32b3e87502d51008a9a8123bd76f9462120a6dff7ae6c6e4 |
| occamy-1.0-Q6_K.gguf | 28514152448 | 43e1b751fe3ebc23bccb72c3183763aabba33741854264033f4e7bb56865c829 |
| occamy-1.0-Q8_0.gguf | 36903139456 | 9bd6eceae8434098c8c7ebec33e4ca0d0bd3f004c2bddf37d16cd906448fdbdd |
| occamy-1.0-IQ3_XS.gguf | 14484144128 | 126b5265fdb52dc53b69be5535c436267cd83acdc87f1820a02ade728d3f5ef4 |
| occamy-1.0-IQ4_NL.gguf | 19779278848 | 6dcf068e182a482b0bdbc493ba7154712a034825d47ebf348d9dee7bece007c0 |
| occamy-1.0-Q4_K_S.gguf | 19889903616 | d7b4dc1b3f8b3be6fa0a16eda8bfb613119afa5a7c8db49d823b59b8af988b0d |
Evaluation source pins and full metrics are in [validation/results.json](validation/results.json). No conclusion about MTP acceleration is drawn from these MTP-disabled runs.

	# Occamy GGUF validation

	Benchmark scope: These measurements use the original qwen35-tagged artifacts. The published copies have qwen2 metadata with identical tensor payloads and separately passed bounded tokenizer and load checks; see [release verification](validation/release.json). Full benchmarks were not repeated after the metadata correction.

	This report covers 12 quantization types and a BF16 reference. It uses existing measurements only. The 60-case screen per artifact contains 24 GSM8K questions, 24 HumanEval+ tasks, 6 strict JSON cases and 6 native-tool cases. All 312 code outputs were actually executed with the pinned base and extended tests in an isolated CPU sandbox. This is a small fixed screen, not a full official benchmark.

	[Machine-readable results](validation/results.json) · [780 per-question results](validation/per_question.jsonl)

	Source: [Accio-Lab/occamy-1.0 at 8f8e0e5](https://huggingface.co/Accio-Lab/occamy-1.0/tree/8f8e0e58a3c9df042be1a3fa2c191fd8047acfb8). Runtime: [llama.cpp at 972d2313](https://github.com/ggml-org/llama.cpp/tree/972d2313bc0bf0a45f634f77d95c9fb03aeab12c). Hardware: NVIDIA B200; one GPU per job; no MTP.

	## Quality results

	\| Artifact \| PPL \| Δ vs direct BF16 \| Math flexible \| Math strict \| Code base+extended \| Strict JSON \| Native tools \| Truncated / 60 \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| BF16 \| 6.238500 \| 0.000000 \| 18/24 \| 0/24 \| 23/24 \| 4/6 \| 6/6 \| 2 \|
	\| IQ2_M \| 7.444683 \| 1.206183 \| 19/24 \| 0/24 \| 22/24 \| 4/6 \| 6/6 \| 2 \|
	\| Q2_K \| 7.418311 \| 1.179811 \| 19/24 \| 0/24 \| 22/24 \| 4/6 \| 6/6 \| 3 \|
	\| IQ3_M \| 6.565706 \| 0.327206 \| 21/24 \| 0/24 \| 22/24 \| 4/6 \| 6/6 \| 0 \|
	\| Q3_K_M \| 6.740173 \| 0.501673 \| 21/24 \| 0/24 \| 22/24 \| 4/6 \| 6/6 \| 0 \|
	\| IQ4_XS \| 6.314710 \| 0.076210 \| 19/24 \| 0/24 \| 23/24 \| 4/6 \| 6/6 \| 1 \|
	\| Q4_K_M \| 6.242854 \| 0.004354 \| 19/24 \| 0/24 \| 24/24 \| 4/6 \| 6/6 \| 1 \|
	\| Q5_K_M \| 6.248758 \| 0.010258 \| 19/24 \| 0/24 \| 23/24 \| 4/6 \| 6/6 \| 1 \|
	\| Q6_K \| 6.259335 \| 0.020835 \| 18/24 \| 0/24 \| 24/24 \| 4/6 \| 6/6 \| 1 \|
	\| Q8_0 \| 6.257434 \| 0.018934 \| 18/24 \| 0/24 \| 23/24 \| 4/6 \| 6/6 \| 2 \|
	\| IQ3_XS \| 6.597194 \| 0.358694 \| 19/24 \| 0/24 \| 24/24 \| 4/6 \| 6/6 \| 3 \|
	\| IQ4_NL \| 6.318163 \| 0.079663 \| 19/24 \| 0/24 \| 23/24 \| 4/6 \| 6/6 \| 3 \|
	\| Q4_K_S \| 6.282276 \| 0.043776 \| 21/24 \| 0/24 \| 23/24 \| 4/6 \| 6/6 \| 0 \|

	Math strict-match uses the unchanged required-format extractor. The prompt did not request `####`; its failures must not be read as zero mathematical ability. Flexible-extract is reported separately using the upstream extractor. JSON is parsed as a complete response; fenced JSON fails strict parsing. Truncation remains part of the denominator. Per-question JSONL contains the original assistant content and tool_calls exactly as stored, alongside outcomes, token usage, truncation, code-test status and response digests. Dataset/task IDs identify prompts; server and infrastructure metadata are excluded.

	PPL uses 32 × 512-token chunks and 8,160 scored tokens from a fixed Wikitext-2 test subset. Direct BF16 PPL is 6.2385, rounded to four decimals. The paired dump contains lossy uint16 log-probabilities: BF16 self-replay 6.238510 versus decoded dump base 6.232933, a 0.005577 floor. Native paired deltas and KL are kept separately in JSON.

	Higher PPL in low-bit variants reflects a measured quality tradeoff, not detected artifact damage. All ten newly quantized artifacts passed full dequantized finite scans of all 733 tensors, and all twelve quantized artifacts were load-tested and benchmarked. Finite/load checks do not guarantee BF16-equivalent answers.

	## Engine throughput

	Native warmup and five repetitions; values are mean ± standard deviation in tokens/s. Eight CPU threads, full GPU offload requested, flash attention, F16 K/V cache, batch 2048 and microbatch 512. Tokenization and sampling are excluded. End-to-end latency and first-token latency for six prompts × three repetitions are available in results.json.

	\| Artifact \| PP512 \| TG128 @512 \| PP2048 \| TG128 @2048 \|
	\|---\|---:\|---:\|---:\|---:\|
	\| BF16 \| 2959.44 ± 74.64 \| 192.96 ± 3.48 \| 3009.48 ± 17.28 \| 193.15 ± 2.79 \|
	\| IQ2_M \| 5495.64 ± 980.55 \| 211.60 ± 8.84 \| 5814.88 ± 229.21 \| 204.74 ± 20.24 \|
	\| Q2_K \| 4806.40 ± 994.10 \| 233.02 ± 6.45 \| 5085.95 ± 122.89 \| 227.00 ± 18.29 \|
	\| IQ3_M \| 6262.15 ± 829.11 \| 206.39 ± 5.20 \| 6522.41 ± 183.47 \| 203.86 ± 9.48 \|
	\| Q3_K_M \| 5998.35 ± 807.62 \| 232.86 ± 2.80 \| 5773.11 ± 897.22 \| 232.02 ± 3.48 \|
	\| IQ4_XS \| 7037.35 ± 984.59 \| 227.52 ± 10.09 \| 7141.30 ± 228.24 \| 228.19 ± 6.50 \|
	\| Q4_K_M \| 6346.99 ± 1065.66 \| 241.55 ± 3.04 \| 6311.66 ± 552.28 \| 238.52 ± 7.65 \|
	\| Q5_K_M \| 6388.11 ± 711.30 \| 237.84 ± 9.99 \| 6310.46 ± 326.16 \| 238.31 ± 9.63 \|
	\| Q6_K \| 5759.14 ± 824.86 \| 223.94 ± 4.21 \| 5765.40 ± 418.06 \| 221.35 ± 9.85 \|
	\| Q8_0 \| 6719.33 ± 1768.73 \| 223.50 ± 4.42 \| 7072.93 ± 340.43 \| 223.21 ± 4.71 \|
	\| IQ3_XS \| 6166.29 ± 1164.82 \| 203.79 ± 2.58 \| 6518.86 ± 339.58 \| 203.77 ± 3.59 \|
	\| IQ4_NL \| 6856.12 ± 1322.34 \| 241.74 ± 12.60 \| 6910.83 ± 666.88 \| 244.75 ± 3.58 \|
	\| Q4_K_S \| 6305.76 ± 1593.44 \| 239.67 ± 4.07 \| 6584.67 ± 270.46 \| 237.58 ± 6.62 \|

	Comparison limits: IQ3_XS, IQ4_NL and Q4_K_S used a shared queue across four B200 cards, with each job on one card. Different cards and simultaneous CPU/IO activity introduce confounding relative to the earlier single-card measurements. Results are screening evidence, not a controlled performance ranking.

	Ten new quantizations share one BF16 input and imatrix. The reused Q4_K_M and Q8_0 artifacts have different historical conversion recipes. Differences therefore cannot be attributed to bit width alone.

	Tokenizer boundary: Benchmark requests use qwen35, aligned to the GGUF BF16 reference. This does not mean they reproduce the HF source tokenizer. Separate small probes found the source uses NFC normalization plus qwen2 behavior and showed disagreement with qwen35. Reused Q4/Q8 receive a qwen35 runtime override without changing their artifact bytes. Existing scores are retained as measurements under that configuration; the correction and its limited validation are documented in [tokenizer review](TOKENIZER.md). Existing Q4_K_M/Q8_0 files remain unchanged; BF16 is not a publication candidate.

	## Exact measured artifacts (before release metadata correction)

	The SHA256 values below identify the original benchmark inputs. Published-file SHA256 values are in [SHA256SUMS-new](SHA256SUMS-new) and [release mapping](validation/release.json). Sizes are file bytes, not runtime memory. BF16 is listed as the measured reference; inclusion in this report does not imply it is uploaded with the quantized artifacts.

	\| Artifact filename \| Bytes \| SHA256 \|
	\|---\|---:\|---\|
	\| occamy-1.0-BF16.gguf \| 69376636992 \| bbad6c5aaf28f20a700748613f998a2c0a98b6df94766ec37bf5c4a2cbf2aadf \|
	\| occamy-1.0-IQ2_M.gguf \| 11659235328 \| 93a72b1723a06b5572d6fe9bafec42dfac1ef8b93cb1f0bf7389143b085ea8d8 \|
	\| occamy-1.0-Q2_K.gguf \| 12939593728 \| caed9d813887780c3b614bec677b46e81acefdf8b13a0cfd6877df4b48ca79f4 \|
	\| occamy-1.0-IQ3_M.gguf \| 15440519168 \| dd0ecc62fc13004e3c1c6094559a8d45387eca8d0b843b8c3e971eca1ed479ce \|
	\| occamy-1.0-Q3_K_M.gguf \| 16764764160 \| a41137554d3d58e56dc2c043650ff92dbaf12822eb8be4a7a0d05ee1e9717d0c \|
	\| occamy-1.0-IQ4_XS.gguf \| 18728777728 \| c37131d9049f031f209f2190bccb482a1d75ad9f2699ef86b67d1a193dbf1eae \|
	\| occamy-1.0-Q4_K_M.gguf \| 21166757696 \| ffb25f763ff9c27f5f4e2adcdef399c5654f9f840fdac33dffe7035ba8266a87 \|
	\| occamy-1.0-Q5_K_M.gguf \| 24729131008 \| 43cb6af4a64e846a32b3e87502d51008a9a8123bd76f9462120a6dff7ae6c6e4 \|
	\| occamy-1.0-Q6_K.gguf \| 28514152448 \| 43e1b751fe3ebc23bccb72c3183763aabba33741854264033f4e7bb56865c829 \|
	\| occamy-1.0-Q8_0.gguf \| 36903139456 \| 9bd6eceae8434098c8c7ebec33e4ca0d0bd3f004c2bddf37d16cd906448fdbdd \|
	\| occamy-1.0-IQ3_XS.gguf \| 14484144128 \| 126b5265fdb52dc53b69be5535c436267cd83acdc87f1820a02ade728d3f5ef4 \|
	\| occamy-1.0-IQ4_NL.gguf \| 19779278848 \| 6dcf068e182a482b0bdbc493ba7154712a034825d47ebf348d9dee7bece007c0 \|
	\| occamy-1.0-Q4_K_S.gguf \| 19889903616 \| d7b4dc1b3f8b3be6fa0a16eda8bfb613119afa5a7c8db49d823b59b8af988b0d \|

	Evaluation source pins and full metrics are in [validation/results.json](validation/results.json). No conclusion about MTP acceleration is drawn from these MTP-disabled runs.