Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+license: mit
+datasets:
+- mlabonne/FineTome-100k
+- efederici/capybara-claude-15k-ita
+language:
+- it
+- en
+library_name: transformers
+pipeline_tag: text-generation
+base_model: microsoft/Phi-3.5-mini-instruct
+tags:
+- trl
+- phi3
+- spectrum
+---
+
+<img src="./assets/phi_35_mini_ita.png" width="450"></img>
+# Phi-3.5-mini-ITA
+
+Fine-tuned version of [Microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) optimized for better performance in Italian.
+
+🔹 Small yet powerful model with 3.82 billion parameters
+🔹 Supports 128k context length
+
+- [💬🇮🇹 Chat with the model on Hugging Face Spaces](https://huggingface.co/spaces/anakin87/Phi-3.5-mini-ITA)
+- [GGUF quants](https://huggingface.co/QuantFactory/Phi-3.5-mini-ITA-GGUF)
+  
+🏋️‍♂️ **Do you want to understand how the model was trained?**
+Check out the [📖 full walkthrough article](https://huggingface.co/blog/anakin87/spectrum) and the accompanying [💻 notebook](./notebooks/training.ipynb)
+
+## 🏆 Evaluation
+
+*Open ITA LLM Leaderboard*
+
+| Model                                 | Parameters | Average | MMLU_IT | ARC_IT | HELLASWAG_IT |
+| ------------------------------------- | ---------- | ------- | ------- | ------ | ------------ |
+| **anakin87/Phi-3.5-mini-ITA**         | **3.82 B**     |**57.67**   | 59.93   | 51.5  | 61.57        |
+| meta-llama/Meta-Llama-3.1-8B-Instruct | 8.03 B     | 56.97   | 58.43   | 48.42  | 64.07        |
+| microsoft/Phi-3.5-mini-instruct       | 3.82 B     | 56.82   | 60.03   | 49.19  | 61.25        |
+
+[Details](https://huggingface.co/spaces/mii-llm/open_ita_llm_leaderboard)
+
+*Pinocchio ITA Leaderboard*
+
+| Model                                 | Parameters | Average   | 
+| ------------------------------------- | ---------- | -------   | 
+| **anakin87/Phi-3.5-mini-ITA**         | **3.82 B** | **57.95** |
+| meta-llama/Meta-Llama-3.1-8B-Instruct | 8.03 B     | 56.93     |
+
+[Details](https://huggingface.co/spaces/mii-llm/pinocchio_ita_leaderboard)
+
+
+## 🎮 Model in action
+### Demo
+[💬🇮🇹 Chat with the model on Hugging Face Spaces](https://huggingface.co/spaces/anakin87/Phi-3.5-mini-ITA)
+
+### Text generation with Transformers
+The model is small, so it runs smoothly on Colab. It is also fine to load the model using quantization.
+
+With `transformers==4.44.2`, `trust_remote_code=True` is needed to incorporate a minor bug fix in `Phi3ForCausalLM`.
+Read [this discussion](https://huggingface.co/microsoft/Phi-3.5-mini-instruct/discussions/9) for more details.
+
+⚡ *The model is compatible with Flash Attention 2, which  accelerates inference. To enable it, uncomment the `attn_implementation` parameter in the code snippet below.*
+
+```python
+# pip install transformers accelerate
+import torch
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+model_id="anakin87/Phi-3.5-mini-ITA"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    # attn_implementation="flash_attention_2",  # UNCOMMENT TO USE FLASH ATTENTION 2
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+user_input = "Puoi spiegarmi brevemente la differenza tra imperfetto e passato prossimo in italiano e quando si usano?"
+messages = [{"role": "user", "content": user_input}]
+outputs = pipe(user_input, max_new_tokens=500, do_sample=True, temperature=0.001)
+print(outputs[0]["generated_text"])
+```
+
+Example output:
+```
+Certamente! Imperfetto e passato prossimo sono due tempi verbali in italiano che si riferiscono a azioni passate, ma hanno sfumature diverse.
+
+Imperfetto:
+- L'imperfetto è usato per descrivere azioni o situazioni passate che erano continue o ripetute nel tempo.
+- Indica un'azione senza una fine specifica o un'azione che si svolgeva abitualmente.
+- È spesso usato per descrivere situazioni, condizioni o stati passati.
+- Esempio: "Quando ero bambino, giocavo spesso nel parco."
+
+Passato Prossimo:
+- Il passato prossimo è usato per descrivere azioni passate che sono state completate o che hanno avuto una durata specifica.
+- Indica un'azione che è avvenuta in un momento specifico nel passato.
+- È spesso usato per descrivere eventi o azioni che hanno una durata definita o che si sono svolte in un momento specifico.
+- Esempio: "Ieri ho finito il libro."
+
+In sintesi, l'imperfetto si usa per azioni continue o abituali nel passato, mentre il passato prossimo si usa per azioni completate o avvenute in un momento specifico nel passato.
+```
+
+### Build AI applications
+You can use the model to create a variety of AI applications.
+
+I recommend using the [🏗️ Haystack LLM framework](https://haystack.deepset.ai/) for orchestration.
+(spoiler: I work on it and it is open-source 😄)
+
+This model is compatible with [`HuggingFaceLocalGenerator`](https://docs.haystack.deepset.ai/docs/huggingfacelocalgenerator) and [`HuggingFaceLocalChatGenerator`](https://docs.haystack.deepset.ai/docs/huggingfacelocalchatgenerator) components.
+You can also deploy the model with a TGI container and then use it with [`HuggingFaceAPIGenerator`](https://docs.haystack.deepset.ai/docs/huggingfaceapigenerator) and the related Chat Generator.
+
+Some examples you can keep inspiration from:
+- [RAG with local open models](https://haystack.deepset.ai/blog/guide-to-using-zephyr-with-haystack2)
+- [Summarization from a Website](https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/hackernews-custom-component-rag.ipynb)
+- [Multilingual RAG](https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/multilingual_rag_podcast.ipynb)
+
+
+## 🔧 Training details
+This model was fine-tuned using HF TRL.
+It underwent 2 epochs of instruction fine-tuning on the [FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) and [Capybara-Claude-15k-ita](https://huggingface.co/datasets/efederici/capybara-claude-15k-ita) datasets. 🙏 Thanks to the authors for providing these datasets.
+
+I adopted a relatively new technique for parameter-efficient learning: [Spectrum](https://arxiv.org/abs/2406.06623).
+The idea is to train only the layers of the model with high Signal-to-Noise Ratio (SNR) and ❄️ freeze the rest.
+
+Training required about 14 hours on a single A6000 GPU.
+
+**For complete training details**, check out the [📖 full walkthrough article](https://huggingface.co/blog/anakin87/spectrum) and the accompanying [💻 notebook](./notebooks/training.ipynb).

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config.json

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