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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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tokenizer = transformers.AutoTokenizer.from_pretrained(model_name) |
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model = transformers.LlamaForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto").eval() |
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def stream(user_prompt): |
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runtimeFlag = "cuda:0" |
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system_prompt = '' |
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B_INST, E_INST = "<s> [INST]", "[/INST]" |
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prompt = f"{system_prompt}{B_INST}{user_prompt.strip()}\n{E_INST}" |
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inputs = tokenizer([prompt], return_tensors="pt").to(runtimeFlag) |
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) |
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_ = model.generate(**inputs, streamer=streamer, max_new_tokens=300, temperature=0.0001, |
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repetition_penalty=1.2, eos_token_id=2, do_sample=True, num_return_sequences=1) |
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domanda = """Scrivi una funzione python che moltiplica per 2 tutti i valori della lista:""" |
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contesto = """ |
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[-5, 10, 15, 20, 25, 30, 35] |
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""" |
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prompt = domanda + "\n" + contesto |
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stream(prompt) |
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``` |
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--- |
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## Developer |
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[Michele Montebovi] |
