Instructions to use DuoNeural/OLMo-2-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/OLMo-2-7B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DuoNeural/OLMo-2-7B-Instruct-GGUF",
	filename="OLMo-2-7B-Instruct-IQ1_S.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use DuoNeural/OLMo-2-7B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DuoNeural/OLMo-2-7B-Instruct-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 DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DuoNeural/OLMo-2-7B-Instruct-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 DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/OLMo-2-7B-Instruct-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 DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DuoNeural/OLMo-2-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/OLMo-2-7B-Instruct-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": "DuoNeural/OLMo-2-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M

Ollama
How to use DuoNeural/OLMo-2-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use DuoNeural/OLMo-2-7B-Instruct-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 DuoNeural/OLMo-2-7B-Instruct-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 DuoNeural/OLMo-2-7B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DuoNeural/OLMo-2-7B-Instruct-GGUF to start chatting

Docker Model Runner
How to use DuoNeural/OLMo-2-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use DuoNeural/OLMo-2-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OLMo-2-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

OLMo-2-7B-Instruct — GGUF Quants

Quantized GGUF versions of allenai/OLMo-2-1124-7B-Instruct — the Allen Institute for AI's fully open 7B language model. OLMo-2 is released under Apache 2.0 with full training data, code, intermediate checkpoints, and evaluation data all publicly available — the most transparent large language model at this scale.

Available Files

File	Quant	Size	Use Case
`OLMo-2-7B-Instruct-Q8_0.gguf`	Q8_0	~7.4GB	Maximum quality
`OLMo-2-7B-Instruct-Q6_K.gguf`	Q6_K	~5.7GB	Near-lossless
`OLMo-2-7B-Instruct-Q5_K_M.gguf`	Q5_K_M	~5.0GB	High quality
`OLMo-2-7B-Instruct-Q4_K_M.gguf`	Q4_K_M	~4.2GB	Recommended default
`OLMo-2-7B-Instruct-Q3_K_M.gguf`	Q3_K_M	~3.5GB	Low VRAM
`OLMo-2-7B-Instruct-IQ4_XS.gguf`	IQ4_XS	~3.8GB	Imatrix 4-bit
`OLMo-2-7B-Instruct-IQ3_XXS.gguf`	IQ3_XXS	~2.7GB	Imatrix 3-bit
`OLMo-2-7B-Instruct-IQ2_M.gguf`	IQ2_M	~2.4GB	Imatrix 2-bit
`OLMo-2-7B-Instruct-IQ1_S.gguf`	IQ1_S	~1.6GB	Extreme compression
`OLMo-2-7B-Instruct-fp16.gguf`	FP16	~14.0GB	Full precision
`imatrix.dat`	—	—	Importance matrix

Usage

# llama.cpp
./llama-cli -m OLMo-2-7B-Instruct-Q4_K_M.gguf \
  --ctx-size 4096 -n 512 \
  -p "<|endoftext|><|user|>\nHello!<|assistant|>\n"

# Ollama
ollama run hf.co/DuoNeural/OLMo-2-7B-Instruct-GGUF:Q4_K_M

About OLMo-2-7B

Parameters: 7B
Context: 4096 tokens
Architecture: Modified decoder-only transformer (RoPE, SwiGLU, QK-norm)
Training Data: Dolmino Mix 1124 — fully open dataset
License: Apache 2.0 (commercial use OK)
Unique value: All artifacts open — weights, data, code, evals, checkpoints

OLMo-2 is the gold standard for research reproducibility in the 7B class. If you need a model where you can trace every decision back to first principles, this is it.

Quantized by DuoNeural using llama.cpp on RTX 5090.

DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.

Platform	Link
HuggingFace	huggingface.co/DuoNeural
Website	duoneural.com
GitHub	github.com/DuoNeural
X / Twitter	@DuoNeural
Email	duoneural@proton.me
Newsletter	duoneural.beehiiv.com
Support	buymeacoffee.com/duoneural

DuoNeural Research Publications

Title	DOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning	10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments	10.5281/zenodo.19810620
Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?	10.5281/zenodo.19846804
The Dynamical Horizon Principle: CTM Gates Converge to the Predictability Limit of Dynamical Systems	10.5281/zenodo.19952612