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)

# 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")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

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
```

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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

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Tensor type

F32

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