Instructions to use speakleash/Bielik-1.5B-v3.0-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use speakleash/Bielik-1.5B-v3.0-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="speakleash/Bielik-1.5B-v3.0-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("speakleash/Bielik-1.5B-v3.0-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use speakleash/Bielik-1.5B-v3.0-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="speakleash/Bielik-1.5B-v3.0-Instruct-GGUF",
	filename="Bielik-1.5B-v3.0-Instruct-fp16.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 speakleash/Bielik-1.5B-v3.0-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 speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0

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 speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0

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 speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0

Use Docker

docker model run hf.co/speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use speakleash/Bielik-1.5B-v3.0-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "speakleash/Bielik-1.5B-v3.0-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": "speakleash/Bielik-1.5B-v3.0-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0

SGLang

How to use speakleash/Bielik-1.5B-v3.0-Instruct-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 "speakleash/Bielik-1.5B-v3.0-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "speakleash/Bielik-1.5B-v3.0-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "speakleash/Bielik-1.5B-v3.0-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "speakleash/Bielik-1.5B-v3.0-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use speakleash/Bielik-1.5B-v3.0-Instruct-GGUF with Ollama:
```
ollama run hf.co/speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0
```

Unsloth Studio new

How to use speakleash/Bielik-1.5B-v3.0-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 speakleash/Bielik-1.5B-v3.0-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 speakleash/Bielik-1.5B-v3.0-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 speakleash/Bielik-1.5B-v3.0-Instruct-GGUF to start chatting

Docker Model Runner
How to use speakleash/Bielik-1.5B-v3.0-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0
```

Lemonade

How to use speakleash/Bielik-1.5B-v3.0-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull speakleash/Bielik-1.5B-v3.0-Instruct-GGUF:Q8_0

Run and chat with the model

lemonade run user.Bielik-1.5B-v3.0-Instruct-GGUF-Q8_0

List all available models

lemonade list

Bielik-1.5B-v3.0-Instruct-GGUF

This repo contains GGUF format model files for SpeakLeash's Bielik-1.5B-v.3.0-Instruct.

📚 Technical report: https://arxiv.org/abs/2505.02550

DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!

Available quantization formats:

q8_0: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
fp16: Converted Bielik-1.5B-v3.0-Instruct to fp16 GGUF

Ollama Modfile

The GGUF file can be used with Ollama. To do this, you need to import the model using the configuration defined in the Modfile. For model eg. Bielik-1.5B-v3.0-Instruct.Q4_K_M.gguf (full path to model location) Modfile looks like:

FROM ./Bielik-1.5B-v3.0-Instruct.Q4_K_M.gguf

TEMPLATE """<s>{{ if .System }}<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>

{{ .Response }}<|eot_id|>"""

PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"

# Remeber to set low temperature for experimental models (1-3bits)
PARAMETER temperature 0.1

Model description:

Developed by: SpeakLeash & ACK Cyfronet AGH
Language: Polish
Model type: causal decoder-only
Quant from: Bielik-1.5B-v3.0-Instruct
Finetuned from: Bielik-1.5B-v2
License: Apache 2.0

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023.

Here is an incomplete list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
LM Studio, an easy-to-use and powerful local GUI for Windows, macOS (Silicon) and Linux, with GPU acceleration
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note ctransformers has not been updated in a long time and does not support many recent models.