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degli LLM: guida pratica per personalizzare un modello","\u003Ch1>\u003Cspan data-sheets-root=\"1\">\u003Cstrong>\u003Cspan class=\"text-color-primary-400\">Fine-tuning degli LLM:\u003C/span> guida pratica per personalizzare un modello\u003C/strong>\u003Cbr />\n\u003C/span>\u003C/h1>\n","Hai un modello linguistico che funziona quasi sempre. Estrae il prodotto, estrae la quantità, e poi su una richiesta come «Ci servono otto monitor» aggiunge una data di consegna inventata. Il campo giusto sarebbe null, e a una persona basterebbe un’occhiata per accorgersene. Per un’applicazione che deve fidarsi del risultato, quell’occhiata, invece, non è un’opzione. […]",30124,"4 Novembre 2026",{"url":985,"width":986,"height":986,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/11/cover-20.png",1254,[988,992,1016,1018],{"block_layout":989,"classes":990,"text":991},"simple_text","above-the-fold","\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Hai un modello linguistico che funziona quasi sempre. Estrae il prodotto, estrae la quantità, e poi su una richiesta come «\u003C/span>\u003Ci>\u003Cspan style=\"font-weight: 400;\">Ci servono otto monitor\u003C/span>\u003C/i>\u003Cspan style=\"font-weight: 400;\">» aggiunge una data di consegna inventata. Il campo giusto sarebbe \u003C/span>\u003Cspan style=\"font-weight: 400;\">null\u003C/span>\u003Cspan style=\"font-weight: 400;\">, e a una persona basterebbe un’occhiata per accorgersene. Per un’applicazione che deve fidarsi del risultato, quell’occhiata, invece, non è un’opzione.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">A questo punto le strade sono più d’una, puoi riscrivere le istruzioni, puoi fornire al modello documenti di riferimento con un sistema \u003C/span>\u003Cb>RAG\u003C/b>\u003Cspan style=\"font-weight: 400;\">, oppure puoi adattare i suoi parametri con il\u003C/span>\u003Cb> fine-tuning\u003C/b>\u003Cspan style=\"font-weight: 400;\">. Non sono alternative esclusive e non rispondono allo stesso problema. In questo articolo vedremo come distinguerle, capendo quando il fine-tuning vale lo sforzo e, se decidi di procedere, a impostarlo con metodo: dai dati alla valutazione, passando per \u003C/span>\u003Cb>LoRA \u003C/b>\u003Cspan style=\"font-weight: 400;\">e \u003C/span>\u003Cb>QLoRA\u003C/b>\u003Cspan style=\"font-weight: 400;\">.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Che cos’è il fine-tuning di un LLM\u003C/strong>\u003C/h2>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Il \u003C/span>\u003Cb>fine-tuning\u003C/b>\u003Cspan style=\"font-weight: 400;\"> è un addestramento successivo che adatta un modello già esistente a dati e obiettivi specifici. Interviene sui parametri, i valori numerici appresi durante l’addestramento. Per capire il punto di partenza puoi approfondire i \u003C/span>\u003Ca href=\"https://datamasters.it/blog/llm-cosa-sono-i-modelli-linguistici-di-grandi-dimensioni/\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">modelli linguistici di grandi dimensioni\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\">.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch3>\u003Cstrong>Pre-training, fine-tuning e inferenza\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Nel pre-training il modello apprende da un corpus generale. Nel fine-tuning viene adattato a esempi successivi. In \u003C/span>\u003Cb>inferenza\u003C/b>\u003Cspan style=\"font-weight: 400;\"> produce risposte usando i parametri disponibili. Una normale conversazione non coincide con un loro aggiornamento.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Il \u003C/span>\u003Ca href=\"https://huggingface.co/docs/trl/sft_trainer\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">supervised fine-tuning documentato da TRL\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\">, o SFT, usa sequenze desiderate per guidare l’apprendimento. Puoi mostrare richieste e risposte oppure conversazioni nel formato atteso dal modello.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Quando conviene fare fine-tuning\u003C/strong>\u003C/h2>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Consideralo quando vuoi ottenere un \u003C/span>\u003Cb>comportamento ricorrente\u003C/b>\u003Cspan style=\"font-weight: 400;\"> su un compito definito e hai esempi statici ed affidabili per mostrarlo. Nell’estrazione dei tre campi, prima prova il modello base con istruzioni che specifichino come trattare le informazioni mancanti.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Con il prompt engineering modifichi istruzioni e contenuti dell’input. Un sistema \u003C/span>\u003Ca href=\"https://arxiv.org/abs/2005.11401\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">RAG\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> recupera documenti e li fornisce al modello, mentre il fine-tuning ne adatta i parametri. I tre approcci possono convivere. Se il problema è accedere a materiali aggiornati e riferimenti verificabili, approfondisci come \u003C/span>\u003Ca href=\"https://datamasters.it/blog/come-creare-e-implementare-un-sistema-rag-da-zero/\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">impostare un sistema RAG da zero\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\">.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Conserva una \u003C/span>\u003Cb>base di confronto\u003C/b>\u003Cspan style=\"font-weight: 400;\"> con le risposte ottenute dal modello originale. Ti servirà per stabilire se l’adattamento corregge gli errori che ti interessano o ne introduce altri.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Quale tecnica di fine-tuning scegliere\u003C/strong>\u003C/h2>\n\u003Ch3>\u003Cstrong>Full fine-tuning: caratteristiche, vantaggi e costi\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Il \u003C/span>\u003Cb>full fine-tuning\u003C/b>\u003Cspan style=\"font-weight: 400;\"> aggiorna tutti i parametri. Richiede risorse diverse dall’adattamento di pochi parametri aggiunti, senza garantire risultati superiori su ogni compito. Il \u003C/span>\u003Ca href=\"https://arxiv.org/abs/2106.09685\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">paper originale di LoRA\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> affronta proprio il problema di rendere più gestibile l’adattamento di modelli grandi.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch3>\u003Cstrong>LoRA e QLoRA per addestrare gli adapter\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cb>LoRA\u003C/b>\u003Cspan style=\"font-weight: 400;\">, Low-Rank Adaptation, congela i pesi pre-addestrati e introduce matrici addestrabili più piccole. Questi parametri aggiunti formano gli adapter. Il rango indica la dimensione della decomposizione utilizzata.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cb>QLoRA\u003C/b>\u003Cspan style=\"font-weight: 400;\">, Quantized Low-Rank Adaptation, combina adapter LoRA con una base congelata quantizzata a 4 bit, cioè rappresentata con precisione ridotta. Il \u003C/span>\u003Ca href=\"https://arxiv.org/abs/2305.14314\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">paper QLoRA\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> descrive il metodo. Gli aggiornamenti riguardano gli adapter, anziché tutti i pesi quantizzati.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch3>\u003Cstrong>PEFT e obiettivo di apprendimento\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Ca href=\"https://huggingface.co/docs/peft/developer_guides/checkpoint\" target=\"_blank\" rel=\"noopener\">\u003Cb>PEFT\u003C/b>\u003C/a>\u003Cspan style=\"font-weight: 400;\">, Parameter-Efficient Fine-Tuning, raccoglie metodi che limitano i parametri da addestrare. LoRA appartiene a questa famiglia. Il Supervised Fine Tuning (\u003C/span>\u003Cb>SFT) \u003C/b>\u003Cspan style=\"font-weight: 400;\">descrive l’apprendimento dagli esempi e può essere realizzato con LoRA o QLoRA.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Come preparare il dataset\u003C/strong>\u003C/h2>\n\u003Ch3>\u003Cstrong>Obiettivo, esempi e formato delle conversazioni\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Definisci lo \u003C/span>\u003Cb>schema dei campi\u003C/b>\u003Cspan style=\"font-weight: 400;\"> prima di raccogliere esempi. Nel nostro esercizio didattico, il prodotto e la quantità devono provenire dalla richiesta, una consegna non specificata deve restare assente.\u003C/span>\u003C/span>\u003C/p>\n\u003Cpre>\u003Cstrong>Input: Ci servono otto monitor.\u003C/strong>\r\n\r\n\u003Cstrong>Output atteso: {\"prodotto\":\"monitor\",\"quantita\":8,\"data_consegna\":null}\u003C/strong>\u003C/pre>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">Aggiungi formulazioni diverse e campi omessi, includendo anche richieste ambigue. Ogni esempio deve mostrare una risposta che consideri corretta.\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Nei dataset conversazionali, \u003C/span>\u003Cspan style=\"font-weight: 400;\">role\u003C/span>\u003Cspan style=\"font-weight: 400;\"> identifica chi parla e \u003C/span>\u003Cspan style=\"font-weight: 400;\">content\u003C/span>\u003Cspan style=\"font-weight: 400;\"> contiene il messaggio. Il \u003C/span>\u003Ca href=\"https://huggingface.co/docs/transformers/chat_templating\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">chat template\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> organizza i messaggi nei token attesi dalla base, le unità in cui viene suddiviso il testo. Modelli diversi possono richiedere strutture differenti.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch3>\u003Cstrong>Training, validazione e test: qualità e separazione\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Usa il training per aggiornare i parametri e la validazione per confrontare configurazioni. Tieni da parte un \u003C/span>\u003Cb>test finale\u003C/b>\u003Cspan style=\"font-weight: 400;\">, da usare per misurare il risultato. \u003C/span>\u003Ca href=\"https://huggingface.co/docs/datasets/process#split\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">Hugging Face Datasets\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> documenta come creare le partizioni.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Controlla errori e dati sensibili prima del training. Rimuovi duplicati e richieste quasi identiche fra le partizioni: il \u003C/span>\u003Ca href=\"https://scikit-learn.org/stable/common_pitfalls.html\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">data leakage\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\">, cioè la contaminazione del confronto con informazioni già usate, può far apparire il modello più efficace su casi nuovi di quanto sia.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cimg fetchpriority=\"high\" decoding=\"async\" class=\"alignnone\" style=\"font-size: 16px; letter-spacing: -0.16px;\" src=\"https://website-prod.datamasters.it/wp-content/uploads/2026/11/inline-01-26.png\" alt=\"fine-tuning LLM\" width=\"851\" height=\"567\" />\u003C/p>\n",{"block_layout":993,"gutter_top":994,"gutter_bottom":994,"list":995,"h_align":1015,"v_align":1015},"cta_list","xl",[996],{"link":997,"config":999,"modal":1003},{"title":998,"url":149,"target":19},"ISCRIVITI ALLA NEWSLETTER",{"variant":161,"theme":1000,"size":1001,"class":1002},"theme-primary","size-default","iscritto-nl qs-link",{"api_url":1004,"title":1005,"text":1006,"name_label":1007,"name_placeholder":1008,"surname_label":1009,"surname_placeholder":1010,"email_label":1011,"email_placeholder":1012,"privacy_text":1013,"submit_text":1014},"/wp-json/dm/v1/modal-form/26876","Iscriviti alla Newletter","\u003Cp>Ricevi direttamente sulla tua mail gli ultimi articoli pubblicati nella nostra sezione AI NEWS per rimanere sempre aggiornato e non perderti nessun contenuto.\u003C/p>\n","Nome","Inserisci il tuo nome","Cognome","Inserisci il tuo cognome","E-mail","Inserisci la tua e-mail","Dichiaro di aver letto l’informativa \u003Ca href=\"https://datamasters.it/privacy-policy/\" target=\"_blank\" rel=\"noopener\">privacy\u003C/a> e di prestare espresso consenso al \u003Ca href=\"https://datamasters.it/trattamento-dati/\" target=\"_blank\" rel=\"noopener\">trattamento dei dati\u003C/a> personali","ISCRIVITI ORA","center",{"block_layout":989,"text":1017},"\u003Ch2>\u003Cstrong>Come eseguire il fine-tuning\u003C/strong>\u003C/h2>\n\u003Ch3>\u003Cstrong>Configurare adapter e iperparametri\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Parti da una \u003C/span>\u003Cb>configurazione documentata\u003C/b>\u003Cspan style=\"font-weight: 400;\"> per il modello scelto. Il learning rate regola l’ampiezza degli aggiornamenti, il batch size gli esempi elaborati insieme e le epoche i passaggi sui dati. Con LoRA scegli anche rango e moduli da adattare. QLoRA aggiunge la quantizzazione della base.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">La guida TRL mostra questo nucleo per il training con adapter:\u003C/span>\u003C/p>\n\u003Cpre>\u003Cstrong>from datasets import load_dataset\u003C/strong>\r\n\r\n\u003Cstrong>from peft import LoraConfig\u003C/strong>\r\n\r\n\u003Cstrong>from trl import SFTTrainer\u003C/strong>\u003C/pre>\n\u003Ch2>\u003C/h2>\n\u003Cpre>\u003Cstrong>trainer = SFTTrainer(\u003C/strong>\r\n\r\n\u003Cstrong>    \"Qwen/Qwen3-0.6B\",\u003C/strong>\r\n\r\n\u003Cstrong>    train_dataset=load_dataset(\"trl-lib/Capybara\", split=\"train\"),\u003C/strong>\r\n\r\n\u003Cstrong>    peft_config=LoraConfig(),\u003C/strong>\r\n\r\n\u003Cstrong>)\u003C/strong>\r\n\r\n\u003Cstrong>trainer.train()\r\n\r\n\u003C/strong>\u003C/pre>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">Lo script Python sopra usa un dataset conversazionale dimostrativo, distinto dagli esempi di estrazione del nostro progetto. Prima di adattarlo, configura dati e valutazione del tuo compito.\u003C/span>\u003C/p>\n\u003Cp> \u003C/p>\n\u003Ch3>\u003Cstrong>Gestire memoria, monitoraggio e salvataggio\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">La \u003C/span>\u003Ca href=\"https://huggingface.co/docs/transformers/grad_accumulation\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">gradient accumulation\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> accumula gradienti su più mini-batch prima di aggiornare i pesi. Il gradiente indica come modificare i parametri rispetto all’obiettivo di training. Il \u003C/span>\u003Ca href=\"https://huggingface.co/docs/transformers/grad_checkpointing\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">gradient checkpointing\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> conserva solo parte delle attivazioni intermedie, ricalcolando le altre per risparmiare memoria.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Osserva memoria e loss, la misura dell’errore sull’obiettivo di addestramento. Salva i checkpoint, cioè gli stati del lavoro. Per riutilizzare l’adapter conserva configurazione, tokenizer e \u003C/span>\u003Cb>riferimento preciso alla base\u003C/b>\u003Cspan style=\"font-weight: 400;\">, compresa la revisione. La \u003C/span>\u003Ca href=\"https://huggingface.co/docs/peft/developer_guides/checkpoint\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">guida ai checkpoint PEFT\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> distingue i parametri dell’adapter dai pesi originali, che servono ancora per caricarlo.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Come valutare il modello fine-tuned\u003C/strong>\u003C/h2>\n\u003Ch3>\u003Cstrong>Confrontare formato, contenuto e casi limite\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Sottoponi modello base e adattato agli stessi esempi riservati, con istruzioni e criteri coerenti. Controlla separatamente \u003C/span>\u003Cb>formato e contenuto\u003C/b>\u003Cspan style=\"font-weight: 400;\">: un JSON valido può riportare una quantità sbagliata.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Nel nostro esercizio, verifica che prodotto e quantità siano fedeli all’input e che la consegna assente diventi \u003C/span>\u003Cspan style=\"font-weight: 400;\">null\u003C/span>\u003Cspan style=\"font-weight: 400;\">. Se la richiesta dice “venerdì”, conserva l’espressione o chiedi il riferimento temporale prima di convertirla in una data. Aggiungi richieste incomplete, ambigue e fuori ambito per cercare anche regressioni.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch3>\u003Cstrong>Metriche automatiche e revisione umana\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Puoi automatizzare la verifica dello schema e il confronto dei campi. \u003C/span>\u003Ca href=\"https://huggingface.co/docs/evaluate/a_quick_tour\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">Hugging Face Evaluate\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> offre strumenti per calcolare metriche del compito. La loss di training misura un obiettivo diverso dalla correttezza di ciascuna estrazione.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Affianca una \u003C/span>\u003Cb>revisione umana\u003C/b>\u003Cspan style=\"font-weight: 400;\"> alle metriche, soprattutto per gli errori che richiedono contesto. Stabilisci anche quali risposte inappropriate o rischiose renderebbero il modello inadatto alla tua applicazione.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cimg decoding=\"async\" class=\"alignnone\" src=\"https://website-prod.datamasters.it/wp-content/uploads/2026/11/inline-02-16.png\" alt=\"fine-tuning LLM\" width=\"722\" height=\"481\" />\u003C/p>\n\u003Ch2>\u003Cstrong>Come distribuire un modello fine-tuned\u003C/strong>\u003C/h2>\n\u003Ch3>\u003Cstrong>Adapter, merge e quantizzazione\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Puoi caricare \u003C/span>\u003Cb>base e adapter\u003C/b>\u003Cspan style=\"font-weight: 400;\"> insieme oppure integrarli mediante il merge, quando metodo e configurazione lo consentono. PEFT documenta \u003C/span>\u003Cspan style=\"font-weight: 400;\">merge_and_unload()\u003C/span>\u003Cspan style=\"font-weight: 400;\">, con vincoli che riguardano anche alcune configurazioni quantizzate.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">La \u003C/span>\u003Cb>quantizzazione\u003C/b>\u003Cspan style=\"font-weight: 400;\"> modifica la rappresentazione numerica dei pesi. Dopo merge o conversioni, ricontrolla gli output e la compatibilità con il motore di inferenza scelto.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch3>\u003Cstrong>Applicazione, API e monitoraggio\u003C/strong>\u003C/h3>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">L’applicazione deve inviare richieste nel formato previsto e gestire le risposte. Un’API è l’interfaccia attraverso cui può comunicare con il servizio del modello.\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\" style=\"font-weight: 400;\">Durante l’uso, registra errori, tempi e risorse secondo i criteri scelti per l’esperimento. Conserva la configurazione distribuita per poter risalire alla versione che ha prodotto una risposta problematica.\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Errori da evitare nel fine-tuning\u003C/strong>\u003C/h2>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Se due revisori ricavano quantità diverse dalla stessa richiesta, correggi le \u003C/span>\u003Cb>annotazioni incoerenti\u003C/b>\u003Cspan style=\"font-weight: 400;\"> prima di aumentare il training. Aggiungere esempi con risposte in conflitto rende meno chiaro il comportamento da apprendere.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Un \u003C/span>\u003Cb>test contaminato\u003C/b>\u003Cspan style=\"font-weight: 400;\"> da dati usati per scegliere la configurazione richiede un nuovo confronto riservato. Se invece i messaggi arrivano al modello nel formato sbagliato, controlla il template. Aumentare epoche e dimensioni della base non sostituisce questi controlli.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Quanto costa fare fine-tuning a un LLM\u003C/strong>\u003C/h2>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Nel budget considera la \u003C/span>\u003Cb>preparazione dei dati\u003C/b>\u003Cspan style=\"font-weight: 400;\">, le risorse di calcolo e i tentativi necessari per valutare le configurazioni. Dopo il training serviranno anche risorse per eseguire il modello.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Misura una \u003C/span>\u003Cb>prova ridotta\u003C/b>\u003Cspan style=\"font-weight: 400;\">, annotando tempo e memoria utilizzati insieme al lavoro di revisione. Usa queste osservazioni per stimare il passaggio successivo. LoRA e QLoRA cambiano il fabbisogno, ma non forniscono un preventivo universale.\u003C/span>\u003C/span>\u003C/p>\n\u003Ch2>\u003Cstrong>Come iniziare con un progetto di fine-tuning\u003C/strong>\u003C/h2>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Prepara un \u003C/span>\u003Cb>set di confronto\u003C/b>\u003Cspan style=\"font-weight: 400;\"> per i tre campi e prova il modello base con istruzioni curate. Sperimenta un adattamento quando hai esempi coerenti e un \u003C/span>\u003Cb>criterio di riuscita\u003C/b>\u003Cspan style=\"font-weight: 400;\"> da verificare.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp class=\"font-size-body-1\">\u003Cspan class=\"text-color-black\">\u003Cspan style=\"font-weight: 400;\">Per approfondire l’integrazione con applicazioni e sistemi RAG, il \u003C/span>\u003Ca href=\"https://datamasters.it/catalogo/percorsi-di-carriera/ai-engineer/\" target=\"_blank\" rel=\"noopener\">\u003Cspan style=\"font-weight: 400;\">Corso AI Engineer\u003C/span>\u003C/a>\u003Cspan style=\"font-weight: 400;\"> include anche fondamenti di fine-tuning. Nel tuo esperimento, annota per ogni richiesta quali campi sono corretti e quali informazioni sono state aggiunte senza essere presenti nell’input.\u003C/span>\u003C/span>\u003C/p>\n\u003Cp> \u003C/p>\n",{"block_layout":1019,"classes":1020,"bg_color":1021,"api_url":1022,"text":1023,"form":1024},"newsletter_form","gutter-mt-xl","color-primary-50","/wp-json/dm/v1/newsletter","\u003Ch6>NEWSLETTER\u003C/h6>\n\u003Cp class=\"font-size-body-1\">Ricevi direttamente sulla tua mail gli ultimi articoli pubblicati nella nostra sezione AI NEWS per rimanere sempre aggiornato e non perderti nessun contenuto.\u003C/p>\n",{"name_label":1007,"name_placeholder":1008,"surname_label":1009,"surname_placeholder":1010,"email_label":1011,"email_placeholder":1012,"privacy_text":1013,"submit_text":1014,"confirm_message":1025,"ac_list":1026,"ac_tags":1027},"Grazie per esserti iscritto alla nostra newsletter!\u003Cbr />\r\nOra riceverai direttamente nella tua casella di posta gli ultimi aggiornamenti dal mondo AI e Data Science per essere sempre al passo con le novità del settore.","710e926224ab6e7d:c45a30d4a57718a233ffb05b912d08180936d4c2:0usa9x2rQSvzXEjmhrYy0Q==","37932bb499f9782c:c070c702439941d8d3486567e5e952c550f619cd:G1Zzq1Cfy1eC5zTOXnsKyg==",{"first_name":1029,"last_name":1030,"avatar":1031,"linkedin_url":1046,"description":1047},"Simone","Truglia",{"ID":1032,"id":1032,"title":1033,"filename":1034,"filesize":1035,"url":1036,"alt":943,"author":1037,"description":19,"caption":19,"name":1033,"status":1038,"uploaded_to":1039,"date":1040,"modified":1041,"menu_order":1042,"mime_type":923,"type":1043,"subtype":1044,"width":1045,"height":1045},1330,"simone-truglia","simone-truglia-1.png",667231,"https://website-prod.datamasters.it/wp-content/uploads/2024/06/simone-truglia-1.png","7","inherit",1313,"2024-06-17 13:55:06","2024-06-17 13:56:20",0,"image","png",1080,"https://www.linkedin.com/in/simonetruglia/","\u003Cp>Simone è un \u003Cstrong>Ingegnere Informatico\u003C/strong> con specializzazione nei \u003Cb>sistemi automatici \u003C/b>e con una grande passione per la \u003Cb>matematica\u003C/b>, la \u003Cb>programmazione\u003C/b> e l’\u003Cb>intelligenza artificiale\u003C/b>. Ha lavorato con diverse aziende europee, aiutandole ad acquisire e ad estrarre il massimo valore dai principali dati a loro disposizione.\u003C/p>\n",[1049,1051,1053],{"label":898,"link":1050},"https://datamasters.it/blog/?tag=fine-tuning",{"label":900,"link":1052},"https://datamasters.it/blog/?tag=llm",{"label":902,"link":1054},"https://datamasters.it/blog/?tag=modelli-ai",[1056,1074,1090,1107,1121,1137,1153,1172,1190,1207,1223,1239],{"id":1057,"image":1058,"raw_date":1060,"date":1061,"date_day":1062,"date_month":1063,"title":1064,"excerpt":1065,"url":1066,"taxonomies":1067,"is_webinar":676},30111,{"url":1059,"width":986,"height":986,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/11/cover-19.png","2026-11-02T16:11:46+01:00","2 Novembre 2026","02","Nov","Unsloth: guida pratica al fine-tuning e all’uso degli LLM","Per ottenere risposte coerenti da un LLM, il primo tentativo è spesso migliorare le istruzioni, specifichi il tono, aggiungi esempi e chiarisci quali informazioni il modello deve chiedere prima di […]","https://datamasters.it/blog/unsloth/",{"category":1068,"tag":1070},[1069],"blog",[1071,1072,1073],"fine-tuning","llm","unsloth",{"id":1075,"image":1076,"raw_date":1078,"date":1079,"date_day":1080,"date_month":1081,"title":1082,"excerpt":1083,"url":1084,"taxonomies":1085,"is_webinar":676},30103,{"url":1077,"width":986,"height":986,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/10/cover-18.png","2026-10-30T12:49:08+01:00","30 Ottobre 2026","30","Ott","LLM in locale: guida per installare e usare modelli AI","Usare un LLM in locale significa eseguire un modello linguistico sul tuo computer, invece di inviare ogni richiesta a un servizio remoto. Puoi tenere i dati sotto il tuo controllo, […]","https://datamasters.it/blog/llm-locale/",{"category":1086,"tag":1087},[],[1072,1088,1089],"llm-in-locale","modelli-ai",{"id":1091,"image":1092,"raw_date":1096,"date":1097,"date_day":1098,"date_month":1081,"title":1099,"excerpt":1100,"url":1101,"taxonomies":1102,"is_webinar":676},30032,{"url":1093,"width":1094,"height":1095,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/10/nemotron-cose-come-funziona-e-quali-modelli-include.jpg",1014,677,"2026-10-28T20:30:41+01:00","28 Ottobre 2026","28","Nemotron: cos’è, come funziona e quali modelli include","Un agente AI che deve controllare un contratto può impiegare pochi secondi per leggere una clausola e molto più tempo per confrontarla con altri documenti. Se poi deve interrogare un […]","https://datamasters.it/blog/nemotron-nvidia/",{"category":1103,"tag":1104},[1069],[1089,1105,1106],"nemotron","nvidia",{"id":1108,"image":1109,"raw_date":1111,"date":1112,"date_day":1113,"date_month":1081,"title":1114,"excerpt":1115,"url":1116,"taxonomies":1117,"is_webinar":676},29896,{"url":1110,"width":986,"height":986,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/10/cover-16.png","2026-10-21T14:52:37+02:00","21 Ottobre 2026","21","Guida a GLM 5.3: cos’è, caratteristiche e come utilizzarlo al meglio","GLM 5.3 è un modello AI di Z.ai pensato per la programmazione e per attività che richiedono più passaggi, come analizzare un progetto software, proporre modifiche e verificarne il risultato. […]","https://datamasters.it/blog/glm-5-3/",{"category":1118,"tag":1119},[],[1120,1089],"glm-5-3",{"id":1122,"image":1123,"raw_date":1125,"date":1126,"date_day":1127,"date_month":1128,"title":1129,"excerpt":1130,"url":1131,"taxonomies":1132,"is_webinar":676},29091,{"url":1124,"width":986,"height":986,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/06/ChatGPT-Image-Jun-15-2026-06_12_50-PM.png","2026-06-15T12:56:59+02:00","15 Giugno 2026","15","Giu","Guida a Ollama, lo strumento open-source per eseguire LLM in locale","Negli ultimi mesi, e ancora di più guardando alle limitazioni presenti e future dei principali provider cloud, molte persone stanno valutando Ollama e strumenti simili per avere un LLM che […]","https://datamasters.it/blog/ollama/",{"category":1133,"tag":1134},[1069],[1072,1135,1136],"ollama","open-source",{"id":1138,"image":1139,"raw_date":1141,"date":1142,"date_day":1143,"date_month":1144,"title":1145,"excerpt":1146,"url":1147,"taxonomies":1148,"is_webinar":676},28959,{"url":1140,"width":986,"height":986,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/05/ChatGPT-Image-May-23-2026-10_35_18-AM.png","2026-05-25T10:43:55+02:00","25 Maggio 2026","25","Mag","Mercury 2: scopri il primo LLM di ragionamento a diffusione","Se un’AI risponde quasi in tempo reale, la prima tentazione è applaudire, la seconda, sicuramente più utile, è chiedersi come ci riesca. Mercury 2 di Inception Labs nasce esattamente su […]","https://datamasters.it/blog/mercury/",{"category":1149,"tag":1150},[1069],[1151,1072,1152],"gpt","mercury-2",{"id":1154,"image":1155,"raw_date":1159,"date":1160,"date_day":1161,"date_month":1162,"title":1163,"excerpt":1164,"url":1165,"taxonomies":1166,"is_webinar":676},28640,{"url":1156,"width":1157,"height":1158,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/03/standard-quality-control-concept-m.jpg",1600,1068,"2026-03-19T10:28:54+01:00","19 Marzo 2026","19","Mar","Quali sono i rischi per la privacy con gli LLM? Le regole della sicurezza dati","I dati personali sono diventati valuta durevole e preziosa e affidarsi ai Large Language Models (LLM) per processarli è sempre più inevitabile. Affascinante, sì. Ma a quale prezzo?  Gli LLM […]","https://datamasters.it/blog/llm-privacy/",{"category":1167,"tag":1168},[1069],[1169,1170,1072,1171],"ai-security","large-language-models","sicurezza-dei-dati",{"id":1173,"image":1174,"raw_date":1177,"date":1178,"date_day":1179,"date_month":1180,"title":1181,"excerpt":1182,"url":1183,"taxonomies":1184,"is_webinar":676},28438,{"url":1175,"width":1176,"height":1176,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/02/copertina-1.png",1024,"2026-02-20T13:14:08+01:00","20 Febbraio 2026","20","Feb","Something Big Is Happening: perché i nuovi modelli AI stanno cambiando il lavoro (e cosa devi fare ora)","Negli ultimi mesi c’è una sensazione strana nell’aria, soprattutto se lavori nel digitale, non si tratta della solita ondata di hype, ma qualcosa di più viscerale, quasi fastidioso. Per anni […]","https://datamasters.it/blog/something-big-is-happening-perche-i-nuovi-modelli-ai-stanno-cambiando-il-lavoro-e-cosa-devi-fare-ora/",{"category":1185,"tag":1186},[1069],[1187,1188,1189,1089],"ai-developer","claude-opus-4-6","gpt-5-3",{"id":1191,"image":1192,"raw_date":1196,"date":1197,"date_day":1198,"date_month":1180,"title":1199,"excerpt":1200,"url":1201,"taxonomies":1202,"is_webinar":676},28283,{"url":1193,"width":1194,"height":1195,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2026/01/teamdatamasters_crea_unimmagine_sullAI_Security_e_di_come_pro_c84af496-7569-45ee-b840-6111cbb75f21_2.png",1232,928,"2026-02-05T08:00:06+01:00","5 Febbraio 2026","05","AI Security: come proteggere LLM ed agenti AI","L’intelligenza artificiale generativa ormai la vediamo ovunque, in azienda la usiamo per scrivere mail, riassumere documenti, rispondere ai clienti, persino per prendere decisioni operative. E, quasi sempre, il motore sotto […]","https://datamasters.it/blog/ai-security-come-proteggere-llm-ed-agenti-ai/",{"category":1203,"tag":1204},[1069],[1205,1169,1072,1206],"ai-agents","prompt-injection",{"id":1208,"image":1209,"raw_date":1212,"date":1213,"date_day":1214,"date_month":1215,"title":1216,"excerpt":1217,"url":1218,"taxonomies":1219,"is_webinar":676},27652,{"url":1210,"width":1157,"height":1211,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2025/09/businessman-pointing-pen-virtual-ai-icon-future-artificial-intelligence-innovation.jpg",900,"2025-09-08T11:37:44+02:00","8 Settembre 2025","08","Set","Flowith: il primo agente AI completo","In una nostra recente analisi abbiamo identificato gli agenti AI come uno dei fenomeni tecnologici più rilevanti del 2025. Mentre molti strumenti di intelligenza artificiale eccellono in compiti specifici, la […]","https://datamasters.it/blog/flowith/",{"category":1220,"tag":1221},[1069],[1205,1222,1072],"flowith",{"id":1224,"image":1225,"raw_date":1228,"date":1229,"date_day":1230,"date_month":1215,"title":1231,"excerpt":1232,"url":1233,"taxonomies":1234,"is_webinar":676},27638,{"url":1226,"width":1157,"height":1227,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2025/09/businessman-using-computer-keyboard-command-create-prompt-with-ai-artificial-technology-include-chatting-bot-message-robot-control-cloud-computing-security-generate-art-concept.jpg",1049,"2025-09-04T11:57:01+02:00","4 Settembre 2025","04","MiniMax-M1: LLM open-source di MiniMax all'avanguardia!","In un settore dominato da giganti tecnologici che spesso custodiscono gelosamente i loro algoritmi, la scelta di MiniMax di rendere M1 open-weight rappresenta una dichiarazione d’intenti. Ma non è solo […]","https://datamasters.it/blog/minimax-m1-llm-open-source-di-minimax-allavanguardia/",{"category":1235,"tag":1236},[1069],[1170,1072,1237,1238],"minimax","minimax-m1",{"id":1240,"image":1241,"raw_date":1244,"date":1245,"date_day":1246,"date_month":1247,"title":1248,"excerpt":1249,"url":1250,"taxonomies":1251,"is_webinar":676},27571,{"url":1242,"width":1157,"height":1243,"alt":19},"https://website-prod.datamasters.it/wp-content/uploads/2025/08/business-person-using-laptop-with-floating-digital-data-graphs-charts-futuristic-tech-background.jpg",914,"2025-08-07T10:39:36+02:00","7 Agosto 2025","07","Ago","Agent Zero: il nuovo agente AI che automatizza ogni cosa","Recentemente, numerose aziende si stanno dedicando allo sviluppo di agenti AI innovativi, dato che a differenza dei chatbot tradizionali a cui siamo ormai abituati, queste nuove soluzioni sono in grado […]","https://datamasters.it/blog/agent-zero-il-nuovo-agente-ai-che-automatizza-ogni-cosa/",{"category":1252,"tag":1253},[1069],[1254,1205,1072],"agent-zero",[1256],{"block_layout":1257,"gutter_top":1258,"gutter_bottom":1258,"bg_color":1259,"fg_color":185,"text":1260,"cta":1261,"small":676},"banner_icon_cta","xxl","color-primary-600","\u003Ch4 class=\"fw-semibold\" style=\"text-align: center;\">ISCRIVITI ED INIZIA\u003Cbr />\nAD IMPARARE SUBITO GRATIS\u003C/h4>\n\u003Cp class=\"font-size-body-1\" style=\"text-align: center;\">Accedi ora a risorse di apprendimento gratuite di alta qualità e trasforma le tue ambizioni\u003Cbr />\nin competenze concrete. Il tuo viaggio verso il successo inizia oggi!\u003C/p>\n",{"link":1262,"config":1264},{"title":119,"url":1263,"target":15},"https://datamasters.it/catalogo/?price=gratuito",{"variant":161,"theme":1265,"size":1001},"theme-white",1793785470364]