Instructions to use GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news with Transformers:

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

pipe = pipeline("text-generation", model="GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news")
model = AutoModelForCausalLM.from_pretrained("GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news

SGLang

How to use GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news 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 "GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news" \
    --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": "GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news",
		"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 "GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news" \
        --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": "GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news with Docker Model Runner:
```
docker model run hf.co/GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news
```

Model Description

This is a 8-bit GPTQ quantized Qwen3-0.6B model. The calibrating dataset contains only hungarian news.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Gábor Madarász
Model type: Transformer
Language(s) (NLP): Hungarian, English
License: apache-2.0
Finetuned from model [optional]: Qwen3-0.6B

Uses

Chat in hungarian with "thinking" mode.

Direct Use

This model is capable of better Hungarian than the original Qwen3-0.6B, but it is not perfect.

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "GaborMadarasz/Qwen3-0.6B-8bit-qptq_hungarian_news"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Szia! Mi lehet az autóbalesetek legfőbb okozója?!"
messages = [
    {"role": "user", "content": "Always answer in hungarian!\n" + prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=568,
    do_sample=True,
    temperature=0.6,
    top_k=20,
    repetition_penalty=1.2,
    top_p=0.96,
    
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)

Evaluation

Compute Infrastructure

Quantized on free Google Colab.

Hardware

1 pcs. NVIDIA T4 GPU

Model Card Contact

gabor.madarasz@gmail.com

Downloads last month: 21

Safetensors

Model size

0.6B params

Tensor type

I32

F16

Model tree for GaborMadarasz/Qwen3-0.6B-8bit-gptq_hungarian_news

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Quantized

(514)

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