{"id":121,"date":"2023-05-02T03:30:56","date_gmt":"2023-05-02T03:30:56","guid":{"rendered":"https:\/\/vulab.com\/?p=121"},"modified":"2023-05-02T03:30:57","modified_gmt":"2023-05-02T03:30:57","slug":"python-code-to-fine-tune-llm","status":"publish","type":"post","link":"https:\/\/vulab.com\/index.php\/2023\/05\/02\/python-code-to-fine-tune-llm\/","title":{"rendered":"Python code to fine tune LLM"},"content":{"rendered":"\n

Example code for fine-tuning a language model using URL content and a vector database, and also an example logic to answer questions with the model using Python.<\/p>\n\n\n\n

Example Code:<\/p>\n\n\n\n

To fine-tune a language model using URL content and a vector database, we can follow these steps:<\/p>\n\n\n\n

    \n
  1. Collect the URL content and preprocess it to prepare it for training.<\/li>\n<\/ol>\n\n\n\n
    import requests\nfrom bs4 import BeautifulSoup\nfrom transformers import AutoTokenizer\nimport re\n\n# Collect URL content\nurl = \"https:\/\/example.com\"\nresponse = requests.get(url)\nsoup = BeautifulSoup(response.content, \"html.parser\")\ntext = soup.get_text()\n\n# Preprocess text\ntext = re.sub(r\"\\s+\", \" \", text)\ntokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\ninputs = tokenizer.encode(text, add_special_tokens=True, return_tensors=\"pt\")<\/code><\/pre>\n\n\n\n
      \n
    1. Store the vector representations of the preprocessed URL content in a vector database, such as Pinecone.<\/li>\n<\/ol>\n\n\n\n
      import pinecone\n\n# Connect to Pinecone\npinecone.init(api_key=\"YOUR_API_KEY\")\n\n# Store vectors in Pinecone\nvectors = model(inputs).last_hidden_state.mean(dim=1)\npinecone.create_index(index_name=\"my_index\", dimension=768)\npinecone.save_index(index_name=\"my_index\", vectors=vectors.numpy())<\/code><\/pre>\n\n\n\n
        \n
      1. Fine-tune the language model using the stored vectors and a task-specific dataset.<\/li>\n<\/ol>\n\n\n\n
        import torch\nfrom transformers import AutoModelForCausalLM, AutoConfig\nfrom torch.utils.data import DataLoader, Dataset\n\n# Define dataset and dataloader\nclass MyDataset(Dataset):\n    def __init__(self, data):\n        self.data = data\n\n    def __getitem__(self, index):\n        return self.data[index]\n\n    def __len__(self):\n        return len(self.data)\n\ndataset = MyDataset([\"This is a sample sentence\", \"Another sample sentence\"])\ndataloader = DataLoader(dataset, batch_size=2)\n\n# Define model and optimizer\nconfig = AutoConfig.from_pretrained(\"gpt2\")\nmodel = AutoModelForCausalLM.from_pretrained(\"gpt2\", config=config)\noptimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\n\n# Fine-tune model\nfor epoch in range(10):\n    for batch in dataloader:\n        inputs = tokenizer.batch_encode_plus(batch, padding=True, return_tensors=\"pt\")\n        input_ids = inputs[\"input_ids\"]\n        attention_mask = inputs[\"attention_mask\"]\n        outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)\n        loss = outputs.loss\n        loss.backward()\n        optimizer.step()\n        optimizer.zero_grad()<\/code><\/pre>\n\n\n\n
        Example Logic for Answering Questions:<\/p>\n\n\n\n
        To use the fine-tuned language model to answer questions, we can follow these steps:<\/p>\n\n\n\n
          \n
        1. Collect the question and preprocess it to prepare it for inference.<\/li>\n<\/ol>\n\n\n\n
          # Collect and preprocess question\nquestion = \"What is the capital of France?\"\nquestion = re.sub(r\"\\s+\", \" \", question)\ninputs = tokenizer.encode(question, add_special_tokens=True, return_tensors=\"pt\")<\/code><\/pre>\n\n\n\n
            \n
          1. Retrieve the most similar vector to the preprocessed question from the vector database.<\/li>\n<\/ol>\n\n\n\n
            # Retrieve most similar vector from Pinecone\nquery_vector = model(inputs).last_hidden_state.mean(dim=1).numpy()\nresult = pinecone.query(index_name=\"my_index\", query=query_vector, top_k=1)<\/code><\/pre>\n\n\n\n
              \n
            1. Generate the answer using the fine-tuned language model and the retrieved vector.<\/li>\n<\/ol>\n\n\n\n
              “`python<\/p>\n\n\n\n
              Generate answer using language model<\/h1>\n\n\n\n
              context = result.ids[<\/p>\n\n\n\n
              context = “Paris is the capital of France. “
              generated = model.generate(
              input_ids=inputs,
              max_length=50,
              do_sample=True,
              top_k=50,
              top_p=0.95,
              temperature=1.0,
              repetition_penalty=1.0,
              pad_token_id=tokenizer.eos_token_id,
              use_cache=True,
              context=context
              )
              answer = tokenizer.decode(generated[0], skip_special_tokens=True)<\/p>\n\n\n\n
              4. Return the answer.<\/code><\/pre>\n\n\n\n
              python<\/p>\n\n\n\n
              Return answer<\/h1>\n\n\n\n
              print(answer)
              “`<\/p>\n\n\n\n
              This logic generates an answer by appending the retrieved context from the vector database to the question and then using the fine-tuned language model to generate a response. The generated response is then decoded to produce the answer. This approach is known as a “prompt-based” or “zero-shot” method for question-answering, where the language model is not explicitly trained on the question-answering task, but rather generates an answer based on a given prompt and some context.<\/p>\n","protected":false},"excerpt":{"rendered":"
              Example code for fine-tuning a language model using URL content and a vector database, and also an example logic to answer questions with the model using Python. Example Code: To fine-tune a language model using URL content and a vector database, we can follow these steps: Example Logic for Answering Questions: To use the fine-tuned …<\/p>\n
                Python code to fine tune LLM<\/span> Read More »<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-global-header-display":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-121","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\nPython code to fine tune LLM -<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/vulab.com\/index.php\/2023\/05\/02\/python-code-to-fine-tune-llm\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python code to fine tune LLM -\" \/>\n<meta property=\"og:description\" content=\"Example code for fine-tuning a language model using URL content and a vector database, and also an example logic to answer questions with the model using Python. 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