|
|
--- |
|
|
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 |
