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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 an updated version of [Mistral-7B-v0.2](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf), specifically fine-tuned with SFT and LoRA adjustments. |
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- It's trained both on publicly available datasets, like [SQUAD-it](https://huggingface.co/datasets/squad_it), and datasets we've created in-house. |
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- it's designed to understand and maintain context, making it ideal for Retrieval Augmented Generation (RAG) tasks and applications requiring contextual awareness. |
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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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Be sure to have transformers, peft and sentencepiece installed |
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```python |
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pip install transformers peft sentencepiece |
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``` |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftModel, PeftConfig |
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device = "cuda" |
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config = PeftConfig.from_pretrained("MoxoffSpA/xxxx") |
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model = AutoModelForCausalLM.from_pretrained("alpindale/Mistral-7B-v0.2-hf") |
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tokenizer = AutoTokenizer.from_pretrained("alpindale/Mistral-7B-v0.2-hf") |
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model = PeftModel.from_pretrained(model, "MoxoffSpA/xxxx") |
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messages = [ |
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{"role": "user", "content": "Qual è il tuo piatto preferito??"}, |
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{"role": "assistant", "content": "Beh, ho un debole per una buona porzione di risotto allo zafferano. È un piatto che si distingue per il suo sapore ricco e il suo bellissimo colore dorato, rendendolo irresistibile!"}, |
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{"role": "user", "content": "Hai delle ricette con il risotto che consigli?"} |
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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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## Bias, Risks and Limitations |
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xxxx has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of |
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responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition |
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of the corpus was used to train the base model (mistralai/Mistral-7B-v0.2), however it is likely to have included a mix of Web data and technical sources |
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like books and code. |
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## Links to resources |
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- SQUAD-it dataset: https://huggingface.co/datasets/squad_it |
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- Mistral_7B_v0.2 original weights: https://models.mistralcdn.com/mistral-7b-v0-2/mistral-7B-v0.2.tar |
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- Mistral_7B_v0.2 model: https://huggingface.co/alpindale/Mistral-7B-v0.2-hf |
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- Open Ita LLM Leaderbord: https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard |
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## Quantized versions |
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We have published as well the 4 bit and 8 bit versions of this model: |
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https://huggingface.co/MoxoffSpA/xxxxQuantized/main |
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## The Moxoff Team |
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Jacopo Abate, Marco D'Ambra, Gianpaolo Francesco Trotta, Luigi Simeone |
