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---
base_model: unsloth/Qwen3-4B
library_name: peft
license: mit
datasets:
- DeepMount00/o1-ITA-REASONING
language:
- it
pipeline_tag: question-answering
---
# Model Card for Model ID
### Model Description
- **Training objective**: Fine-tuned on Italian instruction-style reasoning dataset for better performance in logical, educational, and chain-of-thought tasks.
- **Language(s) (NLP):** Italian
- **License:** MIT
- **Finetuned from model:** unsloth/Qwen3-4B
## Uses
### Direct Use
This model is intended for reasoning-intensive tasks in Italian
## Bias, Risks, and Limitations
- May hallucinate or make factual errors in complex logic chains.
- Not safe for unsupervised use in high-stakes domains like medical/legal reasoning.
- Output quality depends on instruction clarity.
# Training Data
The DeepMount00/o1-ITA-REASONING dataset is crafted to train language models in providing structured, methodical responses to questions in Italian.
Each entry follows a four-step reasoning approach:
- Reasoning: Initial thought process
- Verification: Self-review of the reasoning
- Correction: Amendments if needed
- Final Answer: Conclusive response
The dataset is formatted using XML-like tags to delineate each component, promoting transparency and structured thinking.
It is particularly beneficial for educational purposes, encouraging systematic problem-solving and critical thinking in the Italian language.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from huggingface_hub import login
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
login(token="")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-4B",)
base_model = AutoModelForCausalLM.from_pretrained(
"unsloth/Qwen3-4B",
device_map={"": 0}, token=""
)
model = PeftModel.from_pretrained(base_model,"Rustamshry/ITA-Reasoning-o1")
question = "Quali sono i costi e i benefici ambientali, sociali ed economici dell'energia solare?"
messages = [
{"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
messages,
tokenize = False,
add_generation_prompt = True, # Must add for generation
enable_thinking = True, # Disable thinking
)
from transformers import TextStreamer
_ = model.generate(
**tokenizer(text, return_tensors = "pt").to("cuda"),
max_new_tokens = 2048,
temperature = 0.6,
top_p = 0.95,
top_k = 20,
streamer = TextStreamer(tokenizer, skip_prompt = True),
)
```
### Framework versions
- PEFT 0.14.0 |