Instructions to use BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M",
	filename="fikri-3.1-8B-Instruct-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M: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 BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M: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 BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M:Q4_K_M

LM Studio
Jan
Ollama
How to use BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M with Ollama:
```
ollama run hf.co/BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M:Q4_K_M
```

Unsloth Studio

How to use BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M 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 BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M 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 BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M to start chatting

Docker Model Runner
How to use BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M with Docker Model Runner:
```
docker model run hf.co/BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M:Q4_K_M
```

Lemonade

How to use BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BrewInteractive/fikri-3.1-8B-Instruct-Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.fikri-3.1-8B-Instruct-Q4_K_M-Q4_K_M

List all available models

lemonade list

This is a quantized version of the BrewInteractive/fikri-3.1-8B-Instruct model.

Original model: fikri-3.1-8B-Instruct
Base model: LLaMA-3.1-8B
Quantization: Q4_K_M
Optimized for faster inference and reduced memory usage while maintaining performance
Built on the LLaMA 3.1 architecture (8B)
Fine-tuned for Turkish language tasks
Quantized for improved efficiency

How to use

Install llama.cpp:
- For macOS, use Homebrew:
```
brew install llama.cpp
```
- For other operating systems, follow the installation instructions on the llama.cpp GitHub repository.
Download the quantized GGUF file from this repository's Files section.
Run the following command for conversation mode:

llama-cli -m ./fikri-3.1-8B-Instruct-Q4_K_M.gguf --no-mmap -fa -c 4096 --temp 0.8 -if --in-prefix "<|start_header_id|>user<|end_header_id|>\n\n" --in-suffix "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"

Downloads last month: -

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

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