Instructions to use calcuis/olmo-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use calcuis/olmo-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="calcuis/olmo-gguf",
	filename="OLMo-7B-0724-Instruct-f16.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 calcuis/olmo-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf calcuis/olmo-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf calcuis/olmo-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 calcuis/olmo-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf calcuis/olmo-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 calcuis/olmo-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf calcuis/olmo-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 calcuis/olmo-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf calcuis/olmo-gguf:Q4_K_M

Use Docker

docker model run hf.co/calcuis/olmo-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use calcuis/olmo-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "calcuis/olmo-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": "calcuis/olmo-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/calcuis/olmo-gguf:Q4_K_M

Ollama
How to use calcuis/olmo-gguf with Ollama:
```
ollama run hf.co/calcuis/olmo-gguf:Q4_K_M
```

Unsloth Studio new

How to use calcuis/olmo-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 calcuis/olmo-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 calcuis/olmo-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for calcuis/olmo-gguf to start chatting

Docker Model Runner
How to use calcuis/olmo-gguf with Docker Model Runner:
```
docker model run hf.co/calcuis/olmo-gguf:Q4_K_M
```

Lemonade

How to use calcuis/olmo-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull calcuis/olmo-gguf:Q4_K_M

Run and chat with the model

lemonade run user.olmo-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

GGUF quantized version of OLMo-7B-0724-Instruct Model

project original source: base model

Q_2_K (not nice)

Q_3_K_S (acceptable)

Q_3_K_M is acceptable (good for running with CPU)

Q_3_K_L (acceptable)

Q_4_K_S (okay)

Q_4_K_M is recommanded (balance)

Q_5_K_S (good)

Q_5_K_M (good in general)

Q_6_K is good also; if you want a better result; take this one instead of Q_5_K_M

Q_8_0 which is very good; need a reasonable size of RAM otherwise you might expect a long wait

f16 is similar to the original hf model; opt this one or hf also fine; make sure you have a good machine

how to run it

use any connector for interacting with gguf; i.e., gguf-connector

this picture is from the base model

Downloads last month: 97

GGUF

Model size

7B params

Architecture

olmo

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for calcuis/olmo-gguf

Base model

allenai/OLMo-7B-0724-Instruct-hf

Quantized

(9)

this model

calcuis
/

olmo-gguf

GGUF quantized version of OLMo-7B-0724-Instruct Model

how to run it

Model tree for calcuis/olmo-gguf

Dataset used to train calcuis/olmo-gguf