FLLM_03_instruction_tuning.ipynb

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   "source": [
    "# Instruction-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YwB8OLqiflAl"
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   "source": [
    "import itertools\n",
    "import jsonlines\n",
    "\n",
    "from datasets import load_dataset\n",
    "from pprint import pprint\n",
    "\n",
    "from llama import BasicModelRunner\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load instruction tuned dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "instruction_tuned_dataset = load_dataset(\"tatsu-lab/alpaca\", split=\"train\", streaming=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = 5\n",
    "print(\"Instruction-tuned dataset:\")\n",
    "top_m = list(itertools.islice(instruction_tuned_dataset, m))\n",
    "for j in top_m:\n",
    "  print(j)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two prompt templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt_template_with_input = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{instruction}\n",
    "\n",
    "### Input:\n",
    "{input}\n",
    "\n",
    "### Response:\"\"\"\n",
    "\n",
    "prompt_template_without_input = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{instruction}\n",
    "\n",
    "### Response:\"\"\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hydrate prompts (add data to prompts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_data = []\n",
    "for j in top_m:\n",
    "  if not j[\"input\"]:\n",
    "    processed_prompt = prompt_template_without_input.format(instruction=j[\"instruction\"])\n",
    "  else:\n",
    "    processed_prompt = prompt_template_with_input.format(instruction=j[\"instruction\"], input=j[\"input\"])\n",
    "\n",
    "  processed_data.append({\"input\": processed_prompt, \"output\": j[\"output\"]})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pprint(processed_data[0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save data to jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with jsonlines.open(f'alpaca_processed.jsonl', 'w') as writer:\n",
    "    writer.write_all(processed_data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare non-instruction-tuned vs. instruction-tuned models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path_hf = \"lamini/alpaca\"\n",
    "dataset_hf = load_dataset(dataset_path_hf)\n",
    "print(dataset_hf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_instruct_model = BasicModelRunner(\"meta-llama/Llama-2-7b-hf\")\n",
    "non_instruct_output = non_instruct_model(\"Tell me how to train my dog to sit\")\n",
    "print(\"Not instruction-tuned output (Llama 2 Base):\", non_instruct_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "instruct_model = BasicModelRunner(\"meta-llama/Llama-2-7b-chat-hf\")\n",
    "instruct_output = instruct_model(\"Tell me how to train my dog to sit\")\n",
    "print(\"Instruction-tuned output (Llama 2): \", instruct_output)"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "> Note: This section of the notebook has been updated.\n",
    "\n",
    "Instruction-tuned output (ChatGPT) responds with:\n",
    "\n",
    "> Training your dog to sit is a basic and essential command that can be taught using positive reinforcement. Here's a simple step-by-step guide:\n",
    "> 1. **Prepare Treats:**\n",
    "   Gather small, soft treats that your dog enjoys. Make sure they are easy to chew and won't take too long to eat.\n",
    "> 2. **Find a Quiet Space:**\n",
    "   Choose a quiet area with minimal distractions for the training session. This will help your dog focus better.\n",
    "> 3. **Get Your Dog's Attention:**\n",
    "   Call your dog's name to get their attention. Make sure they are looking at you.\n",
    "> 4. **Use a Treat to Lure:**\n",
    "   Hold a treat close to your dog's nose, and slowly move your hand upward and slightly backward over their head. As you do this, your dog's natural response will be to follow the treat with their nose, causing them to sit.\n",
    "> 5. **Say the Command:**\n",
    "   As your dog starts to sit, say the command \"Sit\" in a clear and firm voice. Use the word consistently every time you want your dog to sit.\n",
    "> 6. **Reward and Praise:**\n",
    "   As soon as your dog sits, immediately reward them with the treat and offer verbal praise. This positive reinforcement will help them associate sitting with positive outcomes.\n",
    "> 7. **Repeat and Practice:**\n",
    "   Repeat the process several times in a row during each training session. Keep the sessions short (around 5-10 minutes) to prevent your dog from losing interest.\n",
    "> 8. **Add Duration:**\n",
    "   Once your dog consistently sits on command, you can gradually increase the duration by waiting a couple of seconds before giving the treat. This helps reinforce the sit command.\n",
    "> 9. **Generalize the Command:**\n",
    "   Practice the \"sit\" command in different locations and with various distractions to help your dog generalize the behavior.\n",
    "> 10. **Be Patient and Consistent:**\n",
    "    Patience and consistency are key in dog training. Always use positive reinforcement, and avoid punishment. If your dog doesn't succeed initially, go back a step and try again.\n",
    "> \n",
    "> Remember that each dog is unique, and some may learn more quickly than others. Adjust your training approach based on your dog's individual needs and progress."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "<div><img src=\"images/fllm_tokenizer.png\" style=\"width: 50%\"/></div>\n",
    "\n",
    "\n",
    "> *Important!* Use the tokenizer that is associated with the model.\n",
    "\n",
    "If the wrong tokenizer is used, the model will get confused since the tokens are mapped to a different set of numbers, that are associated with a different sequence of letters.\n"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "### Try smaller models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/pythia-70m\")\n",
    "model = AutoModelForCausalLM.from_pretrained(\"EleutherAI/pythia-70m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inference(text, model, tokenizer, max_input_tokens=1000, max_output_tokens=100):\n",
    "  # Tokenize\n",
    "  input_ids = tokenizer.encode(\n",
    "          text,\n",
    "          return_tensors=\"pt\",\n",
    "          truncation=True,\n",
    "          max_length=max_input_tokens\n",
    "  )\n",
    "\n",
    "  # Generate\n",
    "  device = model.device\n",
    "  generated_tokens_with_prompt = model.generate(\n",
    "    input_ids=input_ids.to(device),\n",
    "    max_length=max_output_tokens\n",
    "  )\n",
    "\n",
    "  # Decode\n",
    "  generated_text_with_prompt = tokenizer.batch_decode(generated_tokens_with_prompt, skip_special_tokens=True)\n",
    "\n",
    "  # Strip the prompt\n",
    "  generated_text_answer = generated_text_with_prompt[0][len(text):]\n",
    "\n",
    "  return generated_text_answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "finetuning_dataset_path = \"lamini/lamini_docs\"\n",
    "finetuning_dataset = load_dataset(finetuning_dataset_path)\n",
    "print(finetuning_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sample = finetuning_dataset[\"test\"][0]\n",
    "print(test_sample)\n",
    "\n",
    "print(inference(test_sample[\"question\"], model, tokenizer))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare to finetuned small model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "instruction_model = AutoModelForCausalLM.from_pretrained(\"lamini/lamini_docs_finetuned\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(inference(test_sample[\"question\"], instruction_model, tokenizer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mSJMi8I4Sgrw"
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   "outputs": [],
   "source": [
    "# Pssst! If you were curious how to upload your own dataset to Huggingface\n",
    "# Here is how we did it\n",
    "\n",
    "# !pip install huggingface_hub\n",
    "# !huggingface-cli login\n",
    "\n",
    "# import pandas as pd\n",
    "# import datasets\n",
    "# from datasets import Dataset\n",
    "\n",
    "# finetuning_dataset = Dataset.from_pandas(pd.DataFrame(data=finetuning_dataset))\n",
    "# finetuning_dataset.push_to_hub(dataset_path_hf)"
   ]
  }
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