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--- |
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language: |
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- it |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- text generation |
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--- |
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# Mistral-7B-v0.1 for Italian Language Text Generation |
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## Overview |
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`Mistral-7B-v0.1` is a state-of-the-art Large Language Model (LLM) specifically pre-trained for generating text. With its 7 billion parameters, it's built to excel in benchmarks and outperforms even some larger models like the Llama 2 13B. |
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## Model Architecture |
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The Mistral-7B-v0.1 model is a transformer-based model that can handle a variety of tasks including but not limited to translation, summarization, and text completion. It's particularly designed for the Italian language and can be fine-tuned for specific tasks. |
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## Quantized version |
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[DeepMount00/Mistral-Ita-7b-GGUF](https://huggingface.co/DeepMount00/Mistral-Ita-7b-GGUF) |
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## Unique Features for Italian |
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- **Tailored Vocabulary**: The model's vocabulary is fine-tuned to encompass the nuances and diversity of the Italian language. |
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- **Enhanced Understanding**: Mistral-7B is specifically trained to grasp and generate Italian text, ensuring high linguistic and contextual accuracy. |
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## Capabilities |
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- **Vocabulary Size**: 32,000 tokens, allowing for a broad range of inputs and outputs. |
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- **Hidden Size**: 4,096 dimensions, providing rich internal representations. |
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- **Intermediate Size**: 14,336 dimensions, which contributes to the model's ability to process and generate complex sentences. |
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## How to Use |
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How to utilize my Mistral for Italian text generation |
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```python |
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import transformers |
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from transformers import TextStreamer |
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import torch |
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MODEL_NAME = "DeepMount00/Mistral-Ita-7b" |
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16).eval() |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
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def generate_answer(prompt): |
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encoded_input = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], return_tensors="pt").to(device) |
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generated_ids = model.generate(**encoded_input, max_new_tokens=200, do_sample=True, temperature=0.001, eos_token_id=tokenizer.eos_token_id) |
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answer = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) |
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return answer |
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prompt = "Se un mattone pesa 1kg più metà di se stesso, quanto pesa il mattone? Rispondi impostando l'equazione matematica" |
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print(generate_answer(prompt)) |
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``` |
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--- |
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## Developer |
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[Michele Montebovi] |
