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

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 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/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 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

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

GGUF quantizations of Accio-Lab/occamy-1.0. The existing Q4_K_M, Q8_0 and vision projector are unchanged. New quantizations use a calibration importance matrix; labels describe mixed-precision GGUF recipes, not uniform bits for every tensor.

Downloads

Quantization	Exact bytes	GiB	File
IQ2_M	11,659,235,328	10.859	Download
Q2_K	12,939,593,728	12.051	Download
IQ3_M	15,440,519,168	14.380	Download
Q3_K_M	16,764,764,160	15.613	Download
IQ4_XS	18,728,777,728	17.443	Download
Q4_K_M	21,166,757,696	19.713	Download
Q5_K_M	24,729,131,008	23.031	Download
Q6_K	28,514,152,448	26.556	Download
Q8_0	36,903,139,456	34.369	Download
IQ3_XS	14,484,144,128	13.489	Download
IQ4_NL	19,779,278,848	18.421	Download
Q4_K_S	19,889,903,616	18.524	Download
F16 vision projector	899,282,944	0.838	Download

File size is not peak RAM/VRAM. KV cache, context, concurrency and images require additional memory. The projector is optional for text-only use. New quantizations were tested for text/code/JSON/tool use; vision was not retested for them.

Tokenizer and runtime

Read tokenizer compatibility before running. All released files store qwen2. New files were corrected from the converter-inferred qwen35 without changing tensor payloads. These rules differ on some Unicode text. The pinned source tokenizer uses NFC normalization and a qwen2-style rule. The legacy NFC and 14-case findings are preserved in the original README.

The reported benchmark used qwen35 throughout, including --override-kv tokenizer.ggml.pre=str:qwen35 on the legacy Q4_K_M/Q8_0 files to match its BF16 GGUF reference. To reproduce those measurements on the corrected new copies, the same qwen35 override is also required. It is not an out-of-the-box score for those two files or proof of tokenizer equivalence to the Transformers source. See the bounded source comparison and precise reproduction notes in TOKENIZER.md.

For normal source-compatible text input, normalize prompt content with unicodedata.normalize("NFC", text) and use the native qwen2 metadata. For example:

llama-server -m occamy-1.0-Q4_K_M.gguf -ngl 999 -c 8192 -np 1 -fa on --jinja

The new release copies each passed two short GPU load/generation checks with qwen2+NFC. This is not a full quality rerun. Published hashes and release checks are separate from the original benchmark hashes.

Measured validation

Validation report and machine-readable results cover a local BF16 reference and all twelve quantizations. Each has 24 GSM8K questions, 24 HumanEval+ code outputs actually executed in a sandbox, and six strict JSON plus six tool cases. Per-question outputs and results are separate. These small subsets do not reproduce full published benchmark scores. Lower-bit PPL degradation is reported, not treated as file corruption.

Runtime: CUDA llama.cpp 972d2313bc0bf0a45f634f77d95c9fb03aeab12c on NVIDIA B200; eight CPU threads per timed job, full GPU offload, Flash Attention and F16 KV cache. Engine tests use five repetitions at 512/2048-token contexts. The additional three variants ran independent jobs across four B200 cards; cross-card and shared CPU/I/O effects limit comparisons. CPU-only and Apple Silicon runtime compatibility were not established here.

Historical Q4_K_M/Q8_0 conversion and vision smoke evidence remains in TECHNICAL-DETAILS.md, VALIDATION.json and the preserved README.

MTP and license

The Occamy MTP head is separate. These language-model GGUF files do not contain an MTP head; this update does not establish GGUF draft-head compatibility. Apache 2.0.

Downloads last month: 57,038

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Accio-Lab/occamy-1.0-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

Accio-Lab/occamy-1.0

Quantized

(22)

this model