Instructions to use jkim96/Olmo-3-7B-Think-DASHQ-Q2-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 jkim96/Olmo-3-7B-Think-DASHQ-Q2-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 jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_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 jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_M

Use Docker

docker model run hf.co/jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_M

Llama new
LM Studio
Jan

vLLM

How to use jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkim96/Olmo-3-7B-Think-DASHQ-Q2-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": "jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_M

Ollama
How to use jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF with Ollama:
```
ollama run hf.co/jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_M
```
Unsloth Desktop
Docker Model Runner
How to use jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF with Docker Model Runner:
```
docker model run hf.co/jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_M
```

Lemonade

How to use jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF:IQ2_M

Run and chat with the model

lemonade run user.Olmo-3-7B-Think-DASHQ-Q2-GGUF-IQ2_M

List all available models

lemonade list

Atomic Chat

Olmo-3-7B-Think - DASH-Q 2-bit GGUF

llama.cpp GGUF files of allenai/Olmo-3-7B-Think quantized with DASH-Q, at several 2-bit-class sizes. Every file uses only standard llama.cpp tensor types (no tensor above 4 bits) and loads in any recent llama.cpp build.

File	Type	Size	Bits / weight
`Olmo-3-7B-Think-DASHQ-IQ2_XXS.gguf`	IQ2_XXS	2.26 GB	2.48
`Olmo-3-7B-Think-DASHQ-IQ2_XS.gguf`	IQ2_XS	2.58 GB	2.84
`Olmo-3-7B-Think-DASHQ-IQ2_M.gguf`	IQ2_M	2.72 GB	2.99
`Olmo-3-7B-Think-DASHQ-Q2_K_XL.gguf`	Q2_K_XL	2.93 GB	3.22

Perplexity (lower is better)

Type	Model	Size	WikiText-2	C4
IQ2_XXS	llama.cpp IQ2_XXS (imatrix)	2.14 GB	13.53	23.21
IQ2_XXS	unsloth UD-IQ2_XXS	2.25 GB	13.27	22.67
IQ2_XXS	DASH-Q IQ2_XXS	2.26 GB	12.14	21.69
IQ2_XS	llama.cpp IQ2_XS (imatrix)	2.32 GB	12.31	21.50
IQ2_XS	DASH-Q IQ2_XS	2.58 GB	10.96	19.64
IQ2_M	llama.cpp IQ2_M (imatrix)	2.68 GB	11.46	20.07
IQ2_M	unsloth UD-IQ2_M	2.72 GB	11.45	19.99
IQ2_M	DASH-Q IQ2_M	2.72 GB	10.75	19.21
Q2_K_XL	llama.cpp Q2_K (imatrix)	2.86 GB	11.48	20.36
Q2_K_XL	unsloth UD-Q2_K_XL	3.03 GB	11.34	20.02
Q2_K_XL	DASH-Q Q2_K_XL	2.93 GB	10.70	19.21

llama-perplexity, context 2048; WikiText-2 test, C4 validation (256 x 2048 tokens).

Usage

llama-cli -m Olmo-3-7B-Think-DASHQ-Q2_K_XL.gguf -ngl 99 -c 8192

License

Inherits the license of the base model (allenai/Olmo-3-7B-Think).

Downloads last month: 131

GGUF

Model size

7B params

Architecture

olmo2

Hardware compatibility

2-bit

Model tree for jkim96/Olmo-3-7B-Think-DASHQ-Q2-GGUF

Base model

allenai/Olmo-3-1025-7B

Finetuned

allenai/Olmo-3-7B-Think-SFT

Finetuned

allenai/Olmo-3-7B-Think-DPO

Finetuned

allenai/Olmo-3-7B-Think

Quantized

(34)

this model