Instructions to use ReDiX/Qwen-0.6B-Base-ITA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ReDiX/Qwen-0.6B-Base-ITA with Transformers:

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

pipe = pipeline("text-generation", model="ReDiX/Qwen-0.6B-Base-ITA")
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("ReDiX/Qwen-0.6B-Base-ITA")
model = AutoModelForCausalLM.from_pretrained("ReDiX/Qwen-0.6B-Base-ITA", 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 ReDiX/Qwen-0.6B-Base-ITA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ReDiX/Qwen-0.6B-Base-ITA

SGLang

How to use ReDiX/Qwen-0.6B-Base-ITA 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 "ReDiX/Qwen-0.6B-Base-ITA" \
    --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": "ReDiX/Qwen-0.6B-Base-ITA",
		"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 "ReDiX/Qwen-0.6B-Base-ITA" \
        --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": "ReDiX/Qwen-0.6B-Base-ITA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ReDiX/Qwen-0.6B-Base-ITA with Docker Model Runner:
```
docker model run hf.co/ReDiX/Qwen-0.6B-Base-ITA
```

You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Qwen3 0.6B Base - Ita 🇮🇹

This model is a further-pretrained version of Qwen3-0.6B-Base 🚀, specifically trained on 2 billion Italian tokens. The training data includes educational content 📚 carefully filtered from multilingual pre-training datasets. This ensures the model has a strong understanding of the Italian language and its nuances. It also boasts an extended tokenizer ✍️ optimized for Italian.

⚠️ Important Note: This is an experimental model. It may generate content that is dangerous or includes personal information. Please use with caution.

Base Model (Not Instruct) 🤖

This is not an instruct model, meaning it doesn't follow a specific chat template. Instead, it's designed to be fine-tuned for your specific use case 🎯 with the Italian language.

Evaluation Results 📊

Here's a breakdown of the model's performance on various tasks:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_it	2	none	0	acc	↑	0.2566	±	0.0128
		none	0	acc_norm	↑	0.2840	±	0.0132
hellaswag_it	1	none	0	acc	↑	0.3363	±	0.0049
		none	0	acc_norm	↑	0.3994	±	0.0051
m_mmlu_it	0	none	5	acc	↑	0.2699	±	0.0039

How to use this model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "ReDiX/Qwen-0.6B-Base-ITA"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
).eval()

text = "La principale causa del raffreddore"
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=128
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 


content = tokenizer.decode(output_ids[0:], skip_special_tokens=True).strip("\n")


print("content:", content)

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Safetensors

Model size

0.6B params

Tensor type

F32

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