Instructions to use mswaringen/GritLM-7B-GGUF-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mswaringen/GritLM-7B-GGUF-quantized 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 mswaringen/GritLM-7B-GGUF-quantized:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mswaringen/GritLM-7B-GGUF-quantized:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mswaringen/GritLM-7B-GGUF-quantized:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mswaringen/GritLM-7B-GGUF-quantized:Q4_K_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 mswaringen/GritLM-7B-GGUF-quantized:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf mswaringen/GritLM-7B-GGUF-quantized:Q4_K_M

Use Docker

docker model run hf.co/mswaringen/GritLM-7B-GGUF-quantized:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use mswaringen/GritLM-7B-GGUF-quantized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mswaringen/GritLM-7B-GGUF-quantized"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mswaringen/GritLM-7B-GGUF-quantized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mswaringen/GritLM-7B-GGUF-quantized:Q4_K_M

Ollama
How to use mswaringen/GritLM-7B-GGUF-quantized with Ollama:
```
ollama run hf.co/mswaringen/GritLM-7B-GGUF-quantized:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mswaringen/GritLM-7B-GGUF-quantized with Docker Model Runner:
```
docker model run hf.co/mswaringen/GritLM-7B-GGUF-quantized:Q4_K_M
```

Lemonade

How to use mswaringen/GritLM-7B-GGUF-quantized with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mswaringen/GritLM-7B-GGUF-quantized:Q4_K_M

Run and chat with the model

lemonade run user.GritLM-7B-GGUF-quantized-Q4_K_M

List all available models

lemonade list

Atomic Chat

GGUF quants for https://huggingface.co/GritLM/GritLM-7B

GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.

Layers	Context	Template (Text Representation)	Template (Text Generation)
32	32768	<s><\|user\|> {instruction} <\|embed\|> {sample}	<s><\|user\|> {prompt} <\|assistant\|> {response}

Downloads last month: 29

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

8-bit