Instructions to use lacesseapp/Fikra-1B-Nano-v0.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lacesseapp/Fikra-1B-Nano-v0.2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lacesseapp/Fikra-1B-Nano-v0.2-GGUF",
	filename="fikra-1b-nano-v0.2-q4_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lacesseapp/Fikra-1B-Nano-v0.2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lacesseapp/Fikra-1B-Nano-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lacesseapp/Fikra-1B-Nano-v0.2-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 lacesseapp/Fikra-1B-Nano-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lacesseapp/Fikra-1B-Nano-v0.2-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 lacesseapp/Fikra-1B-Nano-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lacesseapp/Fikra-1B-Nano-v0.2-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 lacesseapp/Fikra-1B-Nano-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lacesseapp/Fikra-1B-Nano-v0.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/lacesseapp/Fikra-1B-Nano-v0.2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use lacesseapp/Fikra-1B-Nano-v0.2-GGUF with Ollama:
```
ollama run hf.co/lacesseapp/Fikra-1B-Nano-v0.2-GGUF:Q4_K_M
```

Unsloth Studio

How to use lacesseapp/Fikra-1B-Nano-v0.2-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 lacesseapp/Fikra-1B-Nano-v0.2-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 lacesseapp/Fikra-1B-Nano-v0.2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lacesseapp/Fikra-1B-Nano-v0.2-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use lacesseapp/Fikra-1B-Nano-v0.2-GGUF with Docker Model Runner:
```
docker model run hf.co/lacesseapp/Fikra-1B-Nano-v0.2-GGUF:Q4_K_M
```

Lemonade

How to use lacesseapp/Fikra-1B-Nano-v0.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lacesseapp/Fikra-1B-Nano-v0.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Fikra-1B-Nano-v0.2-GGUF-Q4_K_M

List all available models

lemonade list

Fikra 1B Nano (GGUF Quantized) 🧠

"The Intel Inside for Edge AI."

This is the quantized (compressed) version of Fikra 1B Nano, optimized for edge devices, consumer smartphones, and offline environments. It uses the GGUF format for high-speed inference on CPU.

Developer: Lacesse Ventures
Format: GGUF (Q4_K_M)
Size: ~700MB
Logic: Fine-tuned on GSM8K (Math) and Dolly 15k (Instruction).

🚀 The Easiest Way to Run (Python SDK)

We have built a dedicated SDK to handle the complexity of GGUF for you. It runs 100% offline.

pip install fikra

from fikra import Fikra

# 1. Initialize (Automatically downloads this model to your machine)
brain = Fikra() 

# 2. Reason (Offline)
answer = brain.reason("If I have 3 apples and eat one, how many are left?")
print(answer)
# Output: "You have 2 apples."

🛠️ Manual Usage (llama.cpp)

If you prefer using llama.cpp directly without our SDK:

./main -m fikra-1b-nano-v0.2-q4_k_m.gguf -n 128 -p "User: Why is the sky blue?\nAnswer:"

📦 About this Quantization

Quantization	Size	Perplexity Loss	Recommended
Q4_K_M	~700 MB	Negligible	✅ Yes (Balanced)

License

Apache 2.0. You are free to use this for commercial applications, including embedded hardware and proprietary software.

Built by James Miano / Lacesse Ventures.

Downloads last month: 1

GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for lacesseapp/Fikra-1B-Nano-v0.2-GGUF

Base model

tiiuae/Falcon-E-1B-Base

Finetuned

lacesseapp/Fikra-1B-Nano-v0.2

Quantized

(1)

this model