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install -q transformers datasets accelerate peft sentencepiece\n", + "\n", + "from datasets import load_dataset\n", + "\n", + "\n", + "ds = load_dataset(\"Programmer-RD-AI/genz-slang-pairs-1k\")\n", + "\n", + "print(ds)\n", + "print(ds[\"train\"][0])\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 261, + "referenced_widgets": [ + "a0df01dd63e94929be828312af0cc700", + "c1704819687e4530900c61be0097fcf7", + "18c6c47bfb2e4180b945d96fdad1352a", + "5a789306cb9445ed8888a029e7f78f58", + "57b0bc660eb24fa3a0a3a32d80d8e999", + "a32790fb27b84bb4822cdf2969d052a1", + "e1a64bea49f946b3a25b15baa63937a6", + "1d67fa4c1a784f3e97cca6b4c7afe84d", + "06950eab64d44a3ea4bf127ae5853b40", + "f0b121fcf49d4b51ba9994a88fbbe6d6", + "b6b6d9764a024fc1879bb70a0f817ba2", + "ada7c094ab074065b3785e99dec759ae", + "e68b56e019b34259897ffca75c23106d", + "c6d0edf94acd409ca91fb95f7cee9605", + "7ec9124b942746e8b247cea217555fde", + "f9ee774d13794ce88e17b65cea077973", + 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This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/1.21k [00:00" + ], + "text/html": [ + "\n", + "
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" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=339, training_loss=2.59374394599667, metrics={'train_runtime': 497.9693, 'train_samples_per_second': 5.446, 'train_steps_per_second': 0.681, 'total_flos': 17703547699200.0, 'train_loss': 2.59374394599667, 'epoch': 3.0})" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Inference function" + ], + "metadata": { + "id": "sGuaYLkBOQwK" + } + }, + { + "cell_type": "code", + "source": [ + "import math\n", + "import random\n", + "\n", + "fallback_emojis = [\"😭\", \"🔥\", \"💀\", \"🤯\", \"✨\", \"😍\", \"🥳\", \"😡\", \"💔\"]\n", + "\n", + "def ensure_emoji(text: str) -> str:\n", + " # If there is already an emoji, keep it\n", + " if any(ch in text for ch in \"😭🔥💀🤯✨😍🥳😡💔\"):\n", + " return text.strip()\n", + " # Otherwise, append a random fallback emoji\n", + " return text.strip() + \" \" + random.choice(fallback_emojis)\n", + "\n", + "\n", + "def to_genz(\n", + " text: str,\n", + " max_new_tokens: int | None = None,\n", + " temperature: float = 0.8,\n", + " top_p: float = 0.9,\n", + " mode: str = \"medium\", # \"light\", \"medium\", \"full\"\n", + "):\n", + " # Simple length-based control if not provided\n", + " if max_new_tokens is None:\n", + " base = max(12, len(text.split()) + 5)\n", + " max_new_tokens = min(64, base)\n", + "\n", + " # Style intensity prompt\n", + " if mode == \"light\":\n", + " style_hint = \"Rewrite in slightly Gen Z style, keep it polite, not too cringe, keep meaning same. Add at most one emoji.\\n\"\n", + " elif mode == \"full\":\n", + " style_hint = \"Rewrite in EXTREME Gen Z slang, super dramatic, very casual, but keep the core meaning same. Use 1-2 emojis.\\n\"\n", + " else:\n", + " style_hint = \"Rewrite in clear Gen Z slang, casual tone, keep meaning same, avoid offensive words. Add 1-2 emojis.\\n\"\n", + "\n", + " inp = style_hint + \"Normal: \" + text + \"\\nGen Z:\"\n", + "\n", + " inputs = tokenizer(inp, return_tensors=\"pt\").to(model.device)\n", + "\n", + " output_ids = model.generate(\n", + " **inputs,\n", + " max_new_tokens=max_new_tokens,\n", + " do_sample=True,\n", + " temperature=temperature,\n", + " top_p=top_p,\n", + " num_return_sequences=1,\n", + " pad_token_id=tokenizer.eos_token_id,\n", + " )\n", + "\n", + " out = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n", + "\n", + " # Take only the part after \"Gen Z:\" if present\n", + " if \"Gen Z:\" in out:\n", + " out = out.split(\"Gen Z:\", 1)[-1]\n", + " out = out.strip()\n", + " out = ensure_emoji(out)\n", + " return out\n" + ], + "metadata": { + "id": "ELWj-IjTTVGR" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pip install -q gradio\n", + "import gradio as gr\n", + "\n", + "def genz_interface(text, mode, temperature, max_len):\n", + " return to_genz(\n", + " text,\n", + " max_new_tokens=int(max_len),\n", + " temperature=float(temperature),\n", + " mode=mode,\n", + " )\n", + "\n", + "examples = [\n", + " [\"I am going to college right now.\", \"medium\", 0.8, 32],\n", + " [\"I am very tired after studying all day.\", \"medium\", 0.9, 40],\n", + " [\"The party was amazing and we had a lot of fun.\", \"full\", 0.9, 40],\n", + " [\"I failed my exam and I feel bad.\", \"light\", 0.7, 32],\n", + "]\n", + "\n", + "demo = gr.Interface(\n", + " fn=genz_interface,\n", + " inputs=[\n", + " gr.Textbox(lines=3, label=\"Normal text\", placeholder=\"Type any normal sentence…\"),\n", + " gr.Dropdown(\n", + " choices=[\"light\", \"medium\", \"full\"],\n", + " value=\"medium\",\n", + " label=\"Gen Z intensity\",\n", + " ),\n", + " gr.Slider(0.3, 1.2, value=0.8, step=0.05, label=\"Creativity (temperature)\"),\n", + " gr.Slider(16, 80, value=40, step=4, label=\"Max new tokens\"),\n", + " ],\n", + " outputs=gr.Textbox(lines=5, label=\"Gen Z style\"),\n", + " title=\"Normal ➜ Gen Z Style Transfer\",\n", + " description=\"Convert boring normal English into Gen Z slang with different intensity levels.\",\n", + " examples=examples,\n", + ")\n", + "\n", + "demo.launch(share=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 614 + }, + "id": "dQUDUZNsTuVT", + "outputId": "10d727fe-1217-4991-ea1b-937dbca2e631" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", + "* Running on public URL: https://7e4711af0eee12d901.gradio.live\n", + "\n", + "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "

" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f15faa25", + "outputId": "e4c2f4f2-4f97-4cd1-9f73-668f1283d336" + }, + "source": [ + "model.save_pretrained(\"my_genz_t5_model\")\n", + "print(\"Model saved to: my_genz_t5_model\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model saved to: my_genz_t5_model\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f25adf83" + }, + "source": [ + "### 1. Authenticate with GitHub\n", + "\n", + "To push to GitHub, you'll need to use a [Personal Access Token (PAT)](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token).\n", + "\n", + "**Steps to create a PAT:**\n", + "1. Go to your GitHub settings.\n", + "2. Navigate to `Developer settings` > `Personal access tokens` > `Tokens (classic)`.\n", + "3. Click `Generate new token (classic)`.\n", + "4. Give it a descriptive name (e.g., `Colab-Access`).\n", + "5. Set an expiration (e.g., 30 days or longer if needed).\n", + "6. Grant the `repo` scope (this is essential for pushing).\n", + "7. Click `Generate token` and **copy it immediately** as you won't be able to see it again.\n", + "\n", + "Once you have your PAT, you can store it securely in Colab's secrets manager (click the '🔑' icon on the left panel) with a name like `GH_TOKEN`. Then, run the following cell to make it accessible to your environment." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5eb39d1d" + }, + "source": [ + "from google.colab import userdata\n", + "\n", + "# Replace 'GH_TOKEN' with the name you gave your PAT in Colab secrets\n", + "GH_TOKEN = userdata.get('GH_TOKEN')\n", + "\n", + "# Configure Git with your email and username\n", + "!git config --global user.email \"your_email@example.com\" # Replace with your email\n", + "!git config --global user.name \"Your GitHub Username\" # Replace with your GitHub username\n", + "\n", + "print(\"Git configured with your email and username.\")\n", + "print(\"Personal Access Token loaded from Colab secrets.\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1d037e85" + }, + "source": [ + "### 2. Clone your GitHub repository\n", + "\n", + "Choose the repository where you want to save your model. Replace `your-username` and `your-repo-name` with your actual GitHub username and repository name." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "47de4e45" + }, + "source": [ + "import os\n", + "\n", + "repo_owner = \"your-username\" # Replace with your GitHub username\n", + "repo_name = \"your-repo-name\" # Replace with your repository name\n", + "\n", + "# Construct the clone URL using the PAT for authentication\n", + "repo_url = f\"https://{GH_TOKEN}@github.com/{repo_owner}/{repo_name}.git\"\n", + "\n", + "# Clone the repository\n", + "!git clone {repo_url}\n", + "\n", + "# Change into the repository directory\n", + "%cd {repo_name}\n", + "\n", + "print(f\"Successfully cloned repository: {repo_name}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8657902d" + }, + "source": [ + "### 3. Copy your saved model files into the repository\n", + "\n", + "Your model was saved to a directory named `my_genz_t5_model` in the root of your Colab environment. Now, copy these files into your cloned repository." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aae7504c" + }, + "source": [ + "# The model was saved to './t5-genz-lora' as per the trainer output directory\n", + "# Let's assume the user wants to copy the final PEFT model (my_genz_t5_model)\n", + "\n", + "# Navigate back to the root if you are still in the repo directory\n", + "%cd ..\n", + "\n", + "# Create a directory for your model within the cloned repo if it doesn't exist\n", + "model_target_path = f\"{repo_name}/my_genz_t5_model\"\n", + "!mkdir -p {model_target_path}\n", + "\n", + "# Copy the saved model files (from where 'model.save_pretrained' saved them)\n", + "# It saves the adapter weights and config, not the full base model.\n", + "# The actual PEFT model is saved to the directory specified in save_pretrained.\n", + "# Based on the earlier cell, model.save_pretrained(\"my_genz_t5_model\")\n", + "!cp -r my_genz_t5_model/* {model_target_path}/\n", + "\n", + "print(f\"Model files copied to {model_target_path}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b1a13102" + }, + "source": [ + "### 4. Add, Commit, and Push Changes\n", + "\n", + "Now, you can add the new files, commit them, and push them to your GitHub repository." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6aa2ff8f" + }, + "source": [ + "# Change into the repository directory to perform git operations\n", + "%cd {repo_name}\n", + "\n", + "# Add all new/modified files\n", + "!git add .\n", + "\n", + "# Commit the changes\n", + "!git commit -m \"Add Gen Z T5 LoRA model files\"\n", + "\n", + "# Push the changes to the remote repository\n", + "!git push origin main # Or your default branch name (e.g., master)\n", + "\n", + "print(\"Model successfully pushed to GitHub!\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "tBK3np2gkiG4" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file