Instructions to use occ-ai/OCC-RAG-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use occ-ai/OCC-RAG-0.6B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="occ-ai/OCC-RAG-0.6B-GGUF",
	filename="OCC-RAG-0.6B-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use occ-ai/OCC-RAG-0.6B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf occ-ai/OCC-RAG-0.6B-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 occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf occ-ai/OCC-RAG-0.6B-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 occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use occ-ai/OCC-RAG-0.6B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "occ-ai/OCC-RAG-0.6B-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": "occ-ai/OCC-RAG-0.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M

Ollama
How to use occ-ai/OCC-RAG-0.6B-GGUF with Ollama:
```
ollama run hf.co/occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M
```

Unsloth Studio

How to use occ-ai/OCC-RAG-0.6B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for occ-ai/OCC-RAG-0.6B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for occ-ai/OCC-RAG-0.6B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for occ-ai/OCC-RAG-0.6B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use occ-ai/OCC-RAG-0.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M
```

Lemonade

How to use occ-ai/OCC-RAG-0.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OCC-RAG-0.6B-GGUF-Q4_K_M

List all available models

lemonade list

OCC-RAG-0.6B-GGUF

OCC-RAG

GitHub | Technical Report | Cloud | Base model

GGUF quantizations of occ-ai/OCC-RAG-0.6B for native inference with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes.

OCC-RAG-0.6B is a 0.6B-parameter small language model specialized for faithful, context-grounded question answering: given a question and a set of sources, it produces a structured reasoning trace with explicit source citations, decides whether the context supports an answer, and either answers from the context or abstains. See the base model card for training details and benchmarks.

Files

The underlying architecture is Qwen3 (0.6B). The chat template is embedded in the GGUF, so llama.cpp/Ollama apply it automatically.

File	Quant	Size	Notes
`OCC-RAG-0.6B-Q4_0.gguf`	Q4_0	0.38 GB	4-bit, legacy — smallest
`OCC-RAG-0.6B-Q4_K_M.gguf`	Q4_K_M	0.40 GB	4-bit K-quant — recommended balance
`OCC-RAG-0.6B-Q5_K_M.gguf`	Q5_K_M	0.44 GB	5-bit K-quant
`OCC-RAG-0.6B-Q6_K.gguf`	Q6_K	0.50 GB	6-bit K-quant
`OCC-RAG-0.6B-Q8_0.gguf`	Q8_0	0.64 GB	8-bit — near-lossless
`OCC-RAG-0.6B-F16.gguf`	F16	1.20 GB	16-bit float (full precision)
`OCC-RAG-0.6B-BF16.gguf`	BF16	1.20 GB	16-bit bfloat (lossless base)

For most uses pick Q4_K_M (smallest good quality) or Q8_0 (highest quality at under a GB). All quants are derived from the BF16 base.

Usage — llama.cpp

# Run directly from the Hub (downloads the chosen quant)
llama-cli -hf occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M -p "Hello" -no-cnv

# Or download a file and run it
llama-cli -m OCC-RAG-0.6B-Q4_K_M.gguf -p "Hello" -no-cnv
# (newer llama.cpp: use `llama-completion` for non-interactive runs)

Usage — Ollama

ollama run hf.co/occ-ai/OCC-RAG-0.6B-GGUF:Q4_K_M

Input / output format

The embedded chat template (apply with llama.cpp's --jinja) builds the query/source tokens for you when sources are supplied as documents; alternatively assemble the tokens manually. See the base model card for the full format and a runnable example.

We recommend greedy decoding (--temp 0), the training/evaluation default.

Limitations

Context-grounded only. Trained to answer from the supplied sources and to ignore parametric knowledge — not a general-purpose chat or knowledge model.
Reasoning depth. Training/evaluation are capped at three-hop reasoning; longer chains are out of distribution.
Quantization. Lower-bit quants (Q4) trade some quality for size; prefer Q6_K/Q8_0 when accuracy matters most.

License

Released under the MIT License, inherited from the base model.

Citation

@misc{savkin2026occragoptimalcognitivecore,
  title         = {OCC-RAG: Optimal Cognitive Core for Faithful Question Answering},
  author        = {Maksim Savkin and Mikhail Goncharov and Alexander Gambashidze and Alla Chepurova and Dmitrii Tarasov and Nikita Andriianov and Daria Pugacheva and Vasily Konovalov and Andrey Galichin and Ivan Oseledets},
  year          = {2026},
  eprint        = {2606.00683},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2606.00683}
}