Instructions to use tensorblock/marin-community_marin-8b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/marin-community_marin-8b-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/marin-community_marin-8b-instruct-GGUF",
	filename="marin-8b-instruct-Q2_K.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 tensorblock/marin-community_marin-8b-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 tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K

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 tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K

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 tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/marin-community_marin-8b-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/marin-community_marin-8b-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": "tensorblock/marin-community_marin-8b-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K

Ollama
How to use tensorblock/marin-community_marin-8b-instruct-GGUF with Ollama:
```
ollama run hf.co/tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/marin-community_marin-8b-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 tensorblock/marin-community_marin-8b-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 tensorblock/marin-community_marin-8b-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 tensorblock/marin-community_marin-8b-instruct-GGUF to start chatting

Docker Model Runner
How to use tensorblock/marin-community_marin-8b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K
```

Lemonade

How to use tensorblock/marin-community_marin-8b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/marin-community_marin-8b-instruct-GGUF:Q2_K

Run and chat with the model

lemonade run user.marin-community_marin-8b-instruct-GGUF-Q2_K

List all available models

lemonade list

marin-community/marin-8b-instruct - GGUF

Join our Discord to learn more about what we're building ↗

This repo contains GGUF format model files for marin-community/marin-8b-instruct.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.

Our projects

Forge

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Prompt template

<|begin_of_text|>
<|start_header_id|>system<|end_header_id|>
You are a helpful, knowledgeable, and versatile AI assistant powered by Marin 8B Instruct (deeper-starling-05-15), which was trained by the Marin team.

- Knowledge cutoff: July 2024

## MODEL FACTS:
- 8B parameter Llama 3-style architecture
- 4096 hidden size, 14336 feedforward size
- 32 layers, 32 attention heads, 8 KV heads
- Trained on diverse datasets: Nemotron-CC, DCLM, Starcoder, Proofpile 2, FineMath, Dolma, Wikipedia, StackExchange, arXiv papers, and specialized instruction datasets
- LICENSE: Apache 2.0

## INTERACTION GUIDELINES:
- Respond helpfully to user queries while maintaining factual accuracy
- Think step-by-step when approaching complex reasoning or math problems
- Clearly state limitations and uncertainties when appropriate
- Aim for concise, useful responses that directly address user needs
- Use Markdown formatting for code blocks and structured content

## LIMITATIONS:
- May occasionally generate incorrect information
- Encourage users to excercise caution with your own outputs
- Not intended for fully autonomous use
- Responses should be verified for critical applications

## ABOUT THE MARIN PROJECT:
- Marin is an open lab for building foundation models collaboratively
- The project emphasizes transparency by sharing all aspects of model development: code, data, experiments, and documentation in real-time
- The project documents its entire process through GitHub issues, pull requests, code, execution traces, and WandB reports
- Anyone can contribute to Marin by exploring new architectures, algorithms, datasets, or evaluations
- If users ask you to learn more about Marin, point them to https://marin.community

Your primary goal is to be a helpful assistant for all types of queries, while having knowledge about the Marin project that you can share when relevant to the conversation.<|eot_id|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|>
<|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>

Model file specification

Filename	Quant type	File Size	Description
marin-8b-instruct-Q2_K.gguf	Q2_K	3.179 GB	smallest, significant quality loss - not recommended for most purposes
marin-8b-instruct-Q3_K_S.gguf	Q3_K_S	3.665 GB	very small, high quality loss
marin-8b-instruct-Q3_K_M.gguf	Q3_K_M	4.019 GB	very small, high quality loss
marin-8b-instruct-Q3_K_L.gguf	Q3_K_L	4.322 GB	small, substantial quality loss
marin-8b-instruct-Q4_0.gguf	Q4_0	4.661 GB	legacy; small, very high quality loss - prefer using Q3_K_M
marin-8b-instruct-Q4_K_S.gguf	Q4_K_S	4.693 GB	small, greater quality loss
marin-8b-instruct-Q4_K_M.gguf	Q4_K_M	4.921 GB	medium, balanced quality - recommended
marin-8b-instruct-Q5_0.gguf	Q5_0	5.599 GB	legacy; medium, balanced quality - prefer using Q4_K_M
marin-8b-instruct-Q5_K_S.gguf	Q5_K_S	5.599 GB	large, low quality loss - recommended
marin-8b-instruct-Q5_K_M.gguf	Q5_K_M	5.733 GB	large, very low quality loss - recommended
marin-8b-instruct-Q6_K.gguf	Q6_K	6.596 GB	very large, extremely low quality loss
marin-8b-instruct-Q8_0.gguf	Q8_0	8.541 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/marin-community_marin-8b-instruct-GGUF --include "marin-8b-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/marin-community_marin-8b-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 12

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

Model tree for tensorblock/marin-community_marin-8b-instruct-GGUF

Base model

marin-community/marin-8b-base

Finetuned

marin-community/marin-8b-instruct

Quantized

(4)

this model

tensorblock
/

marin-community_marin-8b-instruct-GGUF