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

e8fe5e2 verified about 2 months ago

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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. 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 · 780 per-question results

Source: Accio-Lab/occamy-1.0 at 8f8e0e5. Runtime: llama.cpp at 972d2313. 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. 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 and release mapping. 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. No conclusion about MTP acceleration is drawn from these MTP-disabled runs.