metadata
license: apache-2.0
language:
- it
tags:
- text-generation-inference
- text generation
Mistral-7B-v0.1 for Italian Language Text Generation
Overview
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.
Model Architecture
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.
Quantized version
DeepMount00/Mistral-Ita-7b-GGUF
Unique Features for Italian
- Tailored Vocabulary: The model's vocabulary is fine-tuned to encompass the nuances and diversity of the Italian language.
- Enhanced Understanding: Mistral-7B is specifically trained to grasp and generate Italian text, ensuring high linguistic and contextual accuracy.
Capabilities
- Vocabulary Size: 32,000 tokens, allowing for a broad range of inputs and outputs.
- Hidden Size: 4,096 dimensions, providing rich internal representations.
- Intermediate Size: 14,336 dimensions, which contributes to the model's ability to process and generate complex sentences.
How to Use
How to utilize my Mistral for Italian text generation
import transformers
from transformers import TextStreamer
import torch
model_name = "DeepMount00/Mistral-Ita-7b"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = transformers.LlamaForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto").eval()
def stream(user_prompt):
runtimeFlag = "cuda:0"
system_prompt = ''
B_INST, E_INST = "<s> [INST]", "[/INST]"
prompt = f"{system_prompt}{B_INST}{user_prompt.strip()}\n{E_INST}"
inputs = tokenizer([prompt], return_tensors="pt").to(runtimeFlag)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=300, temperature=0.0001,
repetition_penalty=1.2, eos_token_id=2, do_sample=True, num_return_sequences=1)
domanda = """Scrivi una funzione python che moltiplica per 2 tutti i valori della lista:"""
contesto = """
[-5, 10, 15, 20, 25, 30, 35]
"""
prompt = domanda + "\n" + contesto
stream(prompt)
Developer
[Michele Montebovi]