metadata

license: apache-2.0
language:
  - it
pipeline_tag: text-generation
tags:
  - text-generation-inference
  - transformers
  - mistral
  - trl
  - sft
datasets:
  - mchl-labs/stambecco_data_it
widget:
  - text: >-
      Di seguito è riportata un'istruzione che descrive un'attività, abbinata ad
      un input che fornisce ulteriore informazione. Scrivi una risposta che
      soddisfi adeguatamente la richiesta. 

      ### Istruzione:

      Suggerisci un'attività serale romantica


      ### Input:


      ### Risposta:
    example_title: Example 1

Model Card for Model ID

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Walid Iguider
Model type: Minerva
License: cc-by-nc-sa-4.0
Finetuned from model : sapienzanlp/Minerva-3B-base-v1.0

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Uses

Sample Code

  from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
  import torch
  torch.random.manual_seed(0)
  # Run text generation pipeline with our next model
  prompt = """Di seguito è riportata un'istruzione che descrive un'attività, abbinata ad un input che fornisce
  ulteriore informazione. Scrivi una risposta che soddisfi adeguatamente la richiesta.
  
  ### Istruzione:
  Suggerisci un'attività serale romantica

  ### Input:
  
  
  ### Risposta:"""
  
  model_id = "walid-iguider/Minerva-3B-Instruct-v1.0"
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  model = AutoModelForCausalLM.from_pretrained(
      model_id, 
      device_map="cuda", 
      torch_dtype="auto", 
      trust_remote_code=True, 
  )
  
  generation_args = {
      "max_new_tokens": 500,
      "return_full_text": False,
      "temperature": 0.0,
      "do_sample": False,
  }
  
  pipe = pipeline(
      "text-generation",
      model=model,
      tokenizer=tokenizer,
  )
  
  output = pipe(prompt, **generation_args)
  print(output[0]['generated_text'])

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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