Instructions to use SandLogicTechnologies/Falcon3-7B-Base-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/Falcon3-7B-Base-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SandLogicTechnologies/Falcon3-7B-Base-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SandLogicTechnologies/Falcon3-7B-Base-GGUF", dtype="auto")

llama-cpp-python

How to use SandLogicTechnologies/Falcon3-7B-Base-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/Falcon3-7B-Base-GGUF",
	filename="Falcon3-7B-Base_Q4_k_m.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SandLogicTechnologies/Falcon3-7B-Base-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/Falcon3-7B-Base-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Falcon3-7B-Base-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 SandLogicTechnologies/Falcon3-7B-Base-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Falcon3-7B-Base-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 SandLogicTechnologies/Falcon3-7B-Base-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/Falcon3-7B-Base-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 SandLogicTechnologies/Falcon3-7B-Base-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/Falcon3-7B-Base-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SandLogicTechnologies/Falcon3-7B-Base-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use SandLogicTechnologies/Falcon3-7B-Base-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SandLogicTechnologies/Falcon3-7B-Base-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/Falcon3-7B-Base-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SandLogicTechnologies/Falcon3-7B-Base-GGUF:Q4_K_M

SGLang

How to use SandLogicTechnologies/Falcon3-7B-Base-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SandLogicTechnologies/Falcon3-7B-Base-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/Falcon3-7B-Base-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SandLogicTechnologies/Falcon3-7B-Base-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/Falcon3-7B-Base-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use SandLogicTechnologies/Falcon3-7B-Base-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/Falcon3-7B-Base-GGUF:Q4_K_M
```

Unsloth Studio new

How to use SandLogicTechnologies/Falcon3-7B-Base-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 SandLogicTechnologies/Falcon3-7B-Base-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 SandLogicTechnologies/Falcon3-7B-Base-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandLogicTechnologies/Falcon3-7B-Base-GGUF to start chatting

Docker Model Runner
How to use SandLogicTechnologies/Falcon3-7B-Base-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/Falcon3-7B-Base-GGUF:Q4_K_M
```

Lemonade

How to use SandLogicTechnologies/Falcon3-7B-Base-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/Falcon3-7B-Base-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Falcon3-7B-Base-GGUF-Q4_K_M

List all available models

lemonade list

Quantized Falcon3-7B-Base Models

This repository provides quantized GGUF versions of Falcon3-7B-Base model. These 4-bit and 5-bit quantized variants retain the original model’s strengths in language understanding, instruction following, code and mathematics tasks, Falcon3-7B-Base supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K while reducing memory and compute requirements—ideal for efficient inference on resource-constrained devices.

Model Overview

Original Model: Falcon3-7B-Base
Quantized Versions:
- Q4_K_M (4-bit quantization)
- Q5_K_M (5-bit quantization)
Architecture: Decoder-only transformer
Base Model: Falcon3-7B-Base
Modalities: Text only
Developer: Technology Innovation Institute
License: falcon-llm-license
Language: English, French, Spanish, Portuguese

Quantization Details

Q4_K_M Version

Approx. ~71% size reduction
Lower memory footprint (~4.26 GB)
Best suited for deployment on edge devices or low-resource GPUs
Slight performance degradation in complex reasoning scenarios

Q5_K_M Version

Approx. ~67% size reduction
Higher fidelity (~4.96 GB)
Better performance retention, recommended when quality is a priority

Key Features

Pretrained on 14 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips.
Text-only instruction-following model optimized for multi-turn scientific question answering.
Includes multilingual data, but with a primary focus on English (plus French, Spanish, Portuguese).
32K token context length with Grouped Query Attention (12 query heads, 4 KV heads) for scalable inference.

Usage

This lightweight variants model is intended for developers and researchers who work on reasoning, coding, mathematics, and multilingual tasks while cutting down memory and compute costs..

llama.cpp (text-only)

./llama-cli -hf SandLogicTechnologies/Falcon3-7B-Base-GGUF -p "Explain the Object Oriented Programming in simple terms"

Recommended Use Cases

Code generation & programming
Assisting with code completion, debugging, or generating small snippets, especially for use in developer tools or coding assistants.
Scientific & technical research
Useful for answering complex scientific questions, solving mathematical problems, and working with STEM content.
Long-context workflows
Good for documents, research papers, logs, transcripts etc., where you need to process or reference up to ~32K tokens in a single input.
Low-resource deployment
Low-resource deployment runs AI models efficiently on limited hardware like CPUs, edge devices, or small GPUs.

Acknowledgments

These quantized models are based on the original work by Technology Innovation Institute development team.

Special thanks to:

The Technology Innovation Institute team for developing and releasing the Falcon3-7B-Base model.
Georgi Gerganov and the entire llama.cpp open-source community for enabling efficient model quantization and inference via the GGUF format.

Contact

For any inquiries or support, please contact us at support@sandlogic.com or visit our Website.

Downloads last month: 16

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit