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--- |
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license: apache-2.0 |
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language: |
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- it |
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- en |
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library_name: transformers |
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tags: |
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- sft |
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- it |
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- mistral |
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- chatml |
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--- |
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# Model Information |
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xxxx is a SFT and LoRA finetuned version of Mistral-7B-v0.2 |
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It has been trained on a mixture of opensource datasets, like SQUAD-it (https://huggingface.co/datasets/squad_it), and some internally made datasets. |
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It is not just a Q&A, it is a Q&A + Context model, with the goal being it being used for RAGs and application in need of a context. |
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# Evaluation |
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We evaluated the model using the same test sets as used for the Open Ita LLM Leaderboard |
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| hellaswag_it acc_norm | arc_it acc_norm | m_mmlu_it 5-shot acc | Average | |
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|:----------------------:| :---------------: | :--------------------: | :-------: | |
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| 0.6067 | 0.4405 | 0.5112 | 0,52 | |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" |
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model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/xxxx") |
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tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/xxxx") |
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question = """Quanto è alta la torre di Pisa?""" |
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context = """ |
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La Torre di Pisa è un campanile del XII secolo, famoso per la sua inclinazione. Alta circa 56 metri. |
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""" |
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prompt = f"Rispondi alla seguente domanda con meno parle possibili basandoti sul contesto fornito. Domanda: {question}, contesto: {context}" |
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messages = [ |
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{"role": "user", "content": prompt}, |
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] |
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") |
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model_inputs = encodeds.to(device) |
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model.to(device) |
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) |
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decoded = tokenizer.batch_decode(generated_ids) |
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print(decoded[0]) |
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``` |
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## The Moxoff Team |
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Marco D'Ambra, Jacopo Abate, Gianpaolo Francesco Trotta |
