Instructions to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="second-state/Bielik-4.5B-v3.0-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("second-state/Bielik-4.5B-v3.0-Instruct-GGUF")
model = AutoModelForCausalLM.from_pretrained("second-state/Bielik-4.5B-v3.0-Instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/Bielik-4.5B-v3.0-Instruct-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 second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/Bielik-4.5B-v3.0-Instruct-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 second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/Bielik-4.5B-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 "second-state/Bielik-4.5B-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": "second-state/Bielik-4.5B-v3.0-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M

SGLang

How to use second-state/Bielik-4.5B-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 "second-state/Bielik-4.5B-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": "second-state/Bielik-4.5B-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 "second-state/Bielik-4.5B-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": "second-state/Bielik-4.5B-v3.0-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with Ollama:
```
ollama run hf.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Bielik-4.5B-v3.0-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Bielik-4.5B-v3.0-Instruct-GGUF / README.md

apepkuss79

Upload README.md with huggingface_hub

5ed9534 verified over 1 year ago

preview code

Raw

History Blame Contribute Delete

4.72 kB

	---
	base_model: speakleash/Bielik-4.5B-v3.0-Instruct
	license: apache-2.0
	model_creator: speakleash
	model_name: Bielik-4.5B-v3.0-Instruct
	quantized_by: Second State Inc.
	language:
	- pl
	library_name: transformers
	inference:
	parameters:
	temperature: 0.4
	---

	<!-- header start -->
	<!-- 200823 -->
	<div style="width: auto; margin-left: auto; margin-right: auto">
	<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
	</div>
	<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
	<!-- header end -->

	# Bielik-4.5B-v3.0-Instruct-GGUF

	## Original Model

	[speakleash/Bielik-4.5B-v3.0-Instruct](https://huggingface.co/speakleash/Bielik-4.5B-v3.0-Instruct)

	## Run with LlamaEdge

	- LlamaEdge version: [v0.18.3](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.18.3)

	- Prompt template

	- Prompt type: `chatml`

	- Prompt string

	```text
	<\|im_start\|>system
	{system_message}<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant
	```

	- Context size: `32000`

	- Run as LlamaEdge service

	```bash
	wasmedge --dir .:. --nn-preload default:GGML:AUTO:Bielik-4.5B-v3.0-Instruct-Q5_K_M.gguf \
	llama-api-server.wasm \
	--model-name Bielik-4.5B-v3.0-Instruct \
	--prompt-template chatml \
	--ctx-size 32000
	```

	- Run as LlamaEdge command app

	```bash
	wasmedge --dir .:. --nn-preload default:GGML:AUTO:Bielik-4.5B-v3.0-Instruct-Q5_K_M.gguf \
	llama-chat.wasm \
	--prompt-template chatml \
	--ctx-size 32000
	```

	## Quantized GGUF Models

	\| Name \| Quant method \| Bits \| Size \| Use case \|
	\| ---- \| ---- \| ---- \| ---- \| ----- \|
	\| [Bielik-4.5B-v3.0-Instruct-Q2_K.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q2_K.gguf) \| Q2_K \| 2 \| 1.77 GB\| smallest, significant quality loss - not recommended for most purposes \|
	\| [Bielik-4.5B-v3.0-Instruct-Q3_K_L.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 2.50 GB\| small, substantial quality loss \|
	\| [Bielik-4.5B-v3.0-Instruct-Q3_K_M.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 2.30 GB\| very small, high quality loss \|
	\| [Bielik-4.5B-v3.0-Instruct-Q3_K_S.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 2.08 GB\| very small, high quality loss \|
	\| [Bielik-4.5B-v3.0-Instruct-Q4_0.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q4_0.gguf) \| Q4_0 \| 4 \| 2.70 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [Bielik-4.5B-v3.0-Instruct-Q4_K_M.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 2.88 GB\| medium, balanced quality - recommended \|
	\| [Bielik-4.5B-v3.0-Instruct-Q4_K_S.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 2.72 GB\| small, greater quality loss \|
	\| [Bielik-4.5B-v3.0-Instruct-Q5_0.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q5_0.gguf) \| Q5_0 \| 5 \| 3.29 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [Bielik-4.5B-v3.0-Instruct-Q5_K_M.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 3.38 GB\| large, very low quality loss - recommended \|
	\| [Bielik-4.5B-v3.0-Instruct-Q5_K_S.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 3.29 GB\| large, low quality loss - recommended \|
	\| [Bielik-4.5B-v3.0-Instruct-Q6_K.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q6_K.gguf) \| Q6_K \| 6 \| 3.91 GB\| very large, extremely low quality loss \|
	\| [Bielik-4.5B-v3.0-Instruct-Q8_0.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-Q8_0.gguf) \| Q8_0 \| 8 \| 5.06 GB\| very large, extremely low quality loss - not recommended \|
	\| [Bielik-4.5B-v3.0-Instruct-f16.gguf](https://huggingface.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF/blob/main/Bielik-4.5B-v3.0-Instruct-f16.gguf) \| f16 \| 16 \| 9.52 GB\| \|

	Quantized with llama.cpp b5201