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"source": [ "# cell 1: install\n", "!pip install -q transformers datasets accelerate torch torchvision sentencepiece\n", "\n", "# For progress bars\n", "!pip install -q tqdm\n" ] }, { "cell_type": "code", "source": [ "# cell 2: upload your train.jsonl if not already present\n", "from google.colab import files\n", "print(\"If you already uploaded train.jsonl, skip the upload step.\")\n", "uploaded = files.upload() # use to upload train.jsonl with {\"input\",\"output\"} lines\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 90 }, "id": "V_uf0myogEdC", "outputId": "7927fb80-99df-4cea-f1c2-f926c5a03a3c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "If you already uploaded train.jsonl, skip the upload step.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving train-50000.jsonl to train-50000.jsonl\n" ] } ] }, { "cell_type": "code", "source": [ "# cell 3: load HF DatasetDict (expects your train.jsonl in /content)\n", "from datasets import load_dataset, DatasetDict\n", "\n", "# If user uploaded as 'train.jsonl', otherwise change filename\n", "ds = load_dataset(\"json\", data_files={\"train\":\"train.jsonl\"})[\"train\"]\n", "\n", "# create a small validation split for quick checks\n", "ds = ds.train_test_split(test_size=0.08, seed=42)\n", "dataset = DatasetDict({\"train\": ds[\"train\"], \"validation\": ds[\"test\"]})\n", "print(dataset)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 223, "referenced_widgets": [ "9f52012b0cae4293b4dee1c05112a0ef", "b88312dc3fb743aa9205bc1b6d8f6cc8", "a7266b923f9541bb885f6c2a0a4bcc03", "45337c055fd449cfa9df8103d6ebcb1c", "48255954570e42c994b61ac1aa5d5736", "8941edbd747c46548ed21342335d34a8", "903c8c4480fe4eae988a01d799d8fa07", "82b0dae381034529b8fbacf7608b9a18", "d7207a8eb4a54580a788ab12aa0bf026", "44cae723bbf5414eb3e8b58a20c1cd4d", "a06d6a5848d04658b004102c3a90cac4" ] }, "id": "r8qUd8bXgROy", "outputId": "9d6802a1-98f9-4b29-cdb9-074d026a03b8" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "9f52012b0cae4293b4dee1c05112a0ef" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['input', 'output'],\n", " num_rows: 46000\n", " })\n", " validation: Dataset({\n", " features: ['input', 'output'],\n", " num_rows: 4000\n", " })\n", "})\n" ] } ] }, { "cell_type": "code", "source": [ "# cell 4: imports and helper functions\n", "import math, time, os, random\n", "from typing import Optional\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from torch.utils.data import DataLoader\n", "from transformers import AutoTokenizer\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "print(\"Device:\", device)\n", "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n", "# gpt2 tokenizer has no pad token; set it\n", "if tokenizer.pad_token is None:\n", " tokenizer.add_special_tokens({\"pad_token\": \"<|pad|>\"})\n", "vocab_size = len(tokenizer)\n", "\n", "def compute_param_count(n_layers, d_model, d_ff, n_heads):\n", " # approximate parameter count\n", " # embeddings: vocab*d_model + pos*d_model\n", " # per layer: attn (qkv + out) ~ 4 * d_model * d_model (approx),\n", " # feedforward ~ 2 * d_model * d_ff\n", " emb = vocab_size * d_model + d_model * 1024 # assume max pos 1024\n", " per_layer = 4 * d_model * d_model + 2 * d_model * d_ff\n", " total = emb + n_layers * per_layer\n", " return total\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 194, "referenced_widgets": [ "380ee297181e45a591dca6a77b161cb0", "6b27cc77a6144d2a838c69e639e9d752", "ff51d1b818654d169637101e0f5de5ba", "0d364fdd91664ecfb83c582dee178b41", "263ff57475154b2ebcdf65eef0cd88d6", "e8798cfd08604941943c9544af8b0182", "b73f75a0918a40a3b1fcbaf7bee19418", "1a62ac6017d345e5922b733cd5396d20", "eb8c6c78b52041b0a436fdcef1096233", "11a2d65a098a46e696d5a785dd75281f", "5ca22fd5b4e24c828ce55fbf33eb7c8f", "ecd8841ce7a8414f93c862ccf4887277", "2e72ca25e764453d8456be33c0ca5584", "4ab20b93ed7c444da6c0356d1beb2121", "e074495d97954850becc16fd338e9618", "f5dc19e8c95547f5bea2b0ceb649e130", "4b729cb735394bf7b8ee469485afcdae", "2ce6592305024abaa39bc9a09b706cdc", "6c96f82454c7486bacc4b2c24bfd4145", "635eba5c86054de09e8fa14dbc0ae398", "bc3f25b9fae040b2bb1651dd8af321fd", "e275ddaa3bf249dc8346e07cd5db4731", "d891e666ef5a4353a03560a5c2aa0216", "b5b63b4d88284c02978d7574cedf8711", "ae2daac4d72e475483593cec2422169b", "26e2b89eee70484983ce92540d5e7fcd", "15f283f8fc064eef96f894c64c112deb", "49390d66b1a540c287e183d9617cda1f", "4d37bb2d479944d79fa3859ae389690c", "fe3df38093e7445b9d38912684fe1fa3", "cfe8933732814ca3802140744a1e020d", "f61a1ce269fc42d9953dcf1b22e43d94", "3adc5b94a91f4292a80126335133909e", "e15fb8630f0242e581afe8fdb87cc7c6", "97eb9899d1df42acb89b0b7af3b69bd5", "997b6990fa3549518e6e5bfcbdb039cf", "a2f53c20beba4d058285bfc20b882353", "078521478ec04a299de276828f62dd33", "445e629f85d74a3fa160681c3bbeb039", "1b708cf4e5bc40ddb9ca766a28fd520f", "3dddf1bb94d34210b5b1578e4ca1760c", "3f231544c6a74a52a4e9e0a39dce09ad", "f2319cfb1d5447639c199b58f111ff88", "589909b140c749379c329bf0919169d6", "fa11d551abeb4bc193740723b5366a7a", "3cc843fce5f24eef8af5d7a5aae26264", "a31aed33720e40e1a764cd310b1fc419", "53809ded4c11407c81a26d248c8db5f0", "042c8ba5e7b640a293fc5c9ba2afbea2", "abbf605310c7488598966acfa07dac84", "b07ebb22b1e148a3b979476aeca3c559", "570a9119c59b41a1b6d67f8d1232c123", "7b766d1009af4ae484961b3d639a605a", "7f8716a1c55f4aa1be3ab51ccbcd779f", "dcb63e4db56d44798f51e71b2834ae01" ] }, "id": "7djO-GLygwhA", "outputId": "a9ef5213-0f55-4d4c-d287-c72b246ccc0e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Device: cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/26.0 [00:000 else 1.0)\n", " filtered_logits, _ = torch.topk(next_logits, k=top_k)\n", " # sample among top_k\n", " probs = F.softmax(next_logits, dim=-1)\n", " next_token = torch.multinomial(probs, num_samples=1)\n", " tokens = torch.cat([tokens, next_token], dim=1)\n", " return tokenizer.decode(tokens[0], skip_special_tokens=True)\n", "\n", "prompt = \"Write a short paragraph about renewable energy adoption in the UK.\"\n", "print(generate(model, prompt, max_new_tokens=80, temperature=0.9, top_k=40))\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ke1aZA_ihdwk", "outputId": "dd1dac0b-70a9-4f8b-90d1-bcdf0626b41e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Write a short paragraph about renewable energy adoption in the UK.\n", "This passage provides a clear, and neutral explanation about renewable energy. It offers context, structured detail, and meaningful insight while avoiding biased or inappropriate content. It offers context, and meaningful insight while avoiding biased or inappropriate content. It offers context, and meaningful insight while avoiding biased or inappropriate content. It offers context, and meaningful insight while avoiding biased or inappropriate content. It offers context, structured detail,\n" ] } ] }, { "cell_type": "code", "source": [ "# cell 11: multi-config runner (quick-and-dirty)\n", "configs = [\n", " {\"name\":\"small\",\"n_layers\":2,\"n_heads\":2,\"d_model\":128,\"d_ff\":512},\n", " {\"name\":\"base\",\"n_layers\":4,\"n_heads\":4,\"d_model\":256,\"d_ff\":1024},\n", " # Only attempt large if you have RAM/GPU\n", " # {\"name\":\"wide\",\"n_layers\":6,\"n_heads\":6,\"d_model\":384,\"d_ff\":1536},\n", "]\n", "\n", "results = []\n", "\n", "for cfg in configs:\n", " print(\"Running config:\", cfg[\"name\"])\n", " approx = compute_param_count(cfg[\"n_layers\"], cfg[\"d_model\"], cfg[\"d_ff\"], cfg[\"n_heads\"])\n", " print(\"Approx params:\", int(approx))\n", " m = TinyGPT(vocab_size, n_layers=cfg[\"n_layers\"], n_heads=cfg[\"n_heads\"],\n", " d_model=cfg[\"d_model\"], d_ff=cfg[\"d_ff\"], max_seq_len=max_len).to(device)\n", " opt = torch.optim.AdamW(m.parameters(), lr=3e-4)\n", " # small 1-epoch train\n", " for epoch in range(1):\n", " m.train()\n", " for i, batch in enumerate(train_loader):\n", " inp = batch[\"input_ids\"].to(device); lab = batch[\"labels\"].to(device)\n", " logits = m(inp)\n", " loss = F.cross_entropy(logits[:, :-1, :].reshape(-1, logits.size(-1)), lab[:,1:].reshape(-1), ignore_index=tokenizer.pad_token_id)\n", " loss.backward(); opt.step(); opt.zero_grad()\n", " if i%100==0:\n", " print(cfg[\"name\"], \"step\", i, \"loss\", loss.item())\n", " val_loss = evaluate(m, val_loader)\n", " sample = generate(m, \"Describe the future of renewable energy in the UK.\", max_new_tokens=60)\n", " results.append({\"config\":cfg[\"name\"], \"val_loss\":val_loss, \"sample\":sample})\n", " # free up memory\n", " del m; torch.cuda.empty_cache()\n", "\n", "print(\"Summary:\")\n", "for r in results: print(r)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UaPjH_Fuhz6L", "outputId": "2f787b20-e966-445f-89fe-efb992bdf8a8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Running config: small\n", "Approx params: 6957312\n", "small step 0 loss 10.936142921447754\n", "small step 100 loss 2.0066487789154053\n", "small step 200 loss 0.7831152081489563\n", "small step 300 loss 0.4042394757270813\n", "small step 400 loss 0.27594664692878723\n", "small step 500 loss 0.23986797034740448\n", "small step 600 loss 0.2129729688167572\n", "small step 700 loss 0.17271828651428223\n", "small step 800 loss 0.1482691466808319\n", "small step 900 loss 0.13474945724010468\n", "small step 1000 loss 0.13562719523906708\n", "small step 1100 loss 0.1322852075099945\n", "small step 1200 loss 0.12768428027629852\n", "small step 1300 loss 0.13074539601802826\n", "small step 1400 loss 0.11563324928283691\n", "small step 1500 loss 0.11895468831062317\n", "small step 1600 loss 0.108633853495121\n", "small step 1700 loss 0.10433203727006912\n", "small step 1800 loss 0.10576470196247101\n", "small step 1900 loss 0.10991769284009933\n", "small step 2000 loss 0.1076023131608963\n", "small step 2100 loss 0.11549754440784454\n", "small step 2200 loss 0.10694943368434906\n", "small step 2300 loss 0.1030394658446312\n", "small step 2400 loss 0.10950098931789398\n", "small step 2500 loss 0.10665386915206909\n", "small step 2600 loss 0.10858049243688583\n", "small step 2700 loss 0.09851081669330597\n", "small step 2800 loss 0.10054293274879456\n", "small step 2900 loss 0.10746119916439056\n", "small step 3000 loss 0.09992803633213043\n", "small step 3100 loss 0.10812430083751678\n", "small step 3200 loss 0.10923600196838379\n", "small step 3300 loss 0.11495324969291687\n", "small step 3400 loss 0.10835824906826019\n", "small step 3500 loss 0.10515566170215607\n", "small step 3600 loss 0.10571396350860596\n", "small step 3700 loss 0.10220973938703537\n", "small step 3800 loss 0.1048131212592125\n", "small step 3900 loss 0.09765665978193283\n", "small step 4000 loss 0.11014576256275177\n", "small step 4100 loss 0.10197490453720093\n", "small step 4200 loss 0.09691217541694641\n", "small step 4300 loss 0.10824652016162872\n", "small step 4400 loss 0.10557755827903748\n", "small step 4500 loss 0.10410422831773758\n", "small step 4600 loss 0.10059218108654022\n", "small step 4700 loss 0.10663764923810959\n", "small step 4800 loss 0.09478699415922165\n", "small step 4900 loss 0.09678255766630173\n", "small step 5000 loss 0.11002504825592041\n", "small step 5100 loss 0.10351943969726562\n", "small step 5200 loss 0.10882539302110672\n", "small step 5300 loss 0.10057085752487183\n", "small step 5400 loss 0.1069759950041771\n", "small step 5500 loss 0.10004132241010666\n", "small step 5600 loss 0.10338238626718521\n", "small step 5700 loss 0.09935469925403595\n", "Running config: base\n", "Approx params: 16273920\n", "base step 0 loss 10.974065780639648\n", "base step 100 loss 0.5244567394256592\n", "base step 200 loss 0.23574890196323395\n", "base step 300 loss 0.2029615044593811\n", "base step 400 loss 0.12244332581758499\n", "base step 500 loss 0.1135944277048111\n", "base step 600 loss 0.10901793837547302\n", "base step 700 loss 0.11430869251489639\n", "base step 800 loss 0.10217247158288956\n", "base step 900 loss 0.11421678960323334\n", "base step 1000 loss 0.1092977374792099\n", "base step 1100 loss 0.10900989919900894\n", "base step 1200 loss 0.11376353353261948\n", "base step 1300 loss 0.10496743023395538\n", "base step 1400 loss 0.10641879588365555\n", "base step 1500 loss 0.10981472581624985\n", "base step 1600 loss 0.11358921974897385\n", "base step 1700 loss 0.11404959112405777\n", "base step 1800 loss 0.11704839766025543\n", "base step 1900 loss 0.09445181488990784\n", "base step 2000 loss 0.10594352334737778\n", "base step 2100 loss 0.10066144168376923\n", "base step 2200 loss 0.11785681545734406\n", "base step 2300 loss 0.10453670471906662\n", "base step 2400 loss 0.11076048016548157\n", "base step 2500 loss 0.1063772514462471\n", "base step 2600 loss 0.10972446203231812\n", "base step 2700 loss 0.11984846740961075\n", "base step 2800 loss 0.11558016389608383\n", "base step 2900 loss 0.1212877705693245\n", "base step 3000 loss 0.10537463426589966\n", "base step 3100 loss 0.09882750362157822\n", "base step 3200 loss 0.09926895797252655\n", "base step 3300 loss 0.10817407071590424\n", "base step 3400 loss 0.114067941904068\n", "base step 3500 loss 0.09316617250442505\n", "base step 3600 loss 0.10326540470123291\n", "base step 3700 loss 0.09724034368991852\n", "base step 3800 loss 0.1076420471072197\n", "base step 3900 loss 0.1068025752902031\n", "base step 4000 loss 0.09833832830190659\n", "base step 4100 loss 0.09890053421258926\n", "base step 4200 loss 0.10683748126029968\n", "base step 4300 loss 0.09769628942012787\n", "base step 4400 loss 0.11143091320991516\n", "base step 4500 loss 0.10214415192604065\n", "base step 4600 loss 0.11255712062120438\n", "base step 4700 loss 0.10782770067453384\n", "base step 4800 loss 0.10992496460676193\n", "base step 4900 loss 0.0991748496890068\n", "base step 5000 loss 0.1007111519575119\n", "base step 5100 loss 0.09442685544490814\n", "base step 5200 loss 0.10488761961460114\n", "base step 5300 loss 0.09393838793039322\n", "base step 5400 loss 0.1020846888422966\n", "base step 5500 loss 0.11793578416109085\n", "base step 5600 loss 0.11351373046636581\n", "base step 5700 loss 0.10859788954257965\n", "Summary:\n", "{'config': 'small', 'val_loss': 0.10365759241580963, 'sample': 'Describe the future of renewable energy in the UK.\\nThis passage provides a clear, and neutral explanation about renewable energy. It offers context, structured detail, and meaningful insight while avoiding biased or inappropriate content. It offers context, structured detail, structured detail, structured detail, structured detail, structured detail, structured detail, structured detail, structured detail, structured'}\n", "{'config': 'base', 'val_loss': 0.10475846354663372, 'sample': 'Describe the future of renewable energy in the UK.\\nThis passage provides a clear, and neutral explanation about renewable energy. It offers context, structured detail, and meaningful insight while avoiding biased or inappropriate content. It offers context, and meaningful insight while avoiding biased or inappropriate content. It offers context, and meaningful insight while avoiding biased or inappropriate content. It'}\n" ] } ] }, { "cell_type": "code", "source": [ "# cell 12: save small model example (for base model)\n", "save_dir = \"./tinygpt_base\"\n", "os.makedirs(save_dir, exist_ok=True)\n", "torch.save(model.state_dict(), os.path.join(save_dir, \"pytorch_model.bin\"))\n", "tokenizer.save_pretrained(save_dir)\n", "\n", "# Optional: convert to HF format (simple script)\n", "# Note: this will not automatically create a Transformers AutoModel repo, but saves checkpoint + tokenizer.\n", "print(\"Saved to\", save_dir)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "k4-rcBPVh94q", "outputId": "94e6bd17-afd6-47e0-8352-6431299c25b1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Saved to ./tinygpt_base\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell 13: Push TinyGPT model to Hugging Face Hub\n", "from huggingface_hub import HfApi, HfFolder, upload_folder\n", "\n", "# Step 1: Login (only needs to be done once per session)\n", "from huggingface_hub import notebook_login\n", "notebook_login()\n", "\n", "# Step 2: Define repo name\n", "hf_repo_name = \"Abdurrahmanesc/tinygpt-base-model\" # <-- change this to your desired repo\n", "\n", "# Step 3: Create repo (skip if already exists)\n", "api = HfApi()\n", "api.create_repo(repo_id=hf_repo_name, exist_ok=True)\n", "\n", "# Step 4: Upload local folder containing model + tokenizer\n", "upload_folder(\n", " folder_path=\"./tinygpt_base\",\n", " repo_id=hf_repo_name,\n", " commit_message=\"Upload TinyGPT base model\"\n", ")\n", "\n", "print(f\"🚀 TinyGPT model uploaded successfully to: https://huggingface.co/{hf_repo_name}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 150, "referenced_widgets": [ "14e69a36fb4a409c8535a339b0cb364e", "95d6b77633384cd1a752166a442a83b6", "55b13c009faf470dbfe52530603a79aa", "1ac91534a5ed4970be6196871f2be040", "c4f9e4c264e94c62b737acaff2c8c775", "936f01d81c484a7c8ea5f455e7a63e6e", "b6be4e19602b4168b116d445bde77ad9", "d6f24bd763644c03804764e18b642367", "26563ac09615470689726f97a5c43f91", "11e8627edc5046328ae16f7f315e8db2", "1d1fca66dcc2424f86d20ca6b7cb8604", "c7296bbfb73f425f9cfa538a12564722", "f3217fc0c1ec49d0821694ce94a4d674", "2f51666cb65b491594f50b092186f7ed", "cb1fc876416c4905b406f489069087d1", "06da6f6794f6417bb1978f0d8d7c558a", "628a631c347a4b78be5d6079616555a6", "bd35fa9d9d7e4509ac252c0622d3cc7b", "a088dbcf1a334ca2a7e1d185869ef488", "55c9d349f42642a798cfaf088072929a", "6e066ae08505455a8cfe2a72fd30e5ce", "d174280a4bc94bfb8de2fee838a935f6", "11653f75d150432f942d475affee8770", "f6fb93f0f73a45d5a9e9069171f4a350", "2a0da366009c4505baa4a6141838ad32", "424f39af879b4b5e98685e801b663857", "d65fb8edbe2846cc90cd6a1c879b3407", "e18f16f3c48144519051fbaad8887a4a", "6c5598eed422416d96b6721bfc4cf431", "7ea67e6296424f059c4850e2fd5bc650", "840facc820c24863a90d41aaeb957c67", "33f716e5d2bf47b48b704d4e34f0b602", "fe2d91cfcb8b4e6ba88de7db977618e9", "42192f5259c64d2ba909e0d2e7212b65", "c3300b53ac9746dcad09a2b32a056b6f", "f314a42f7b3d453a8c8289b567dc695f", "4fd21e0ecf4546afad2bd50bc72e58e9", "03663ad72b8b4618ba74bb5e21dc92e5", "d1530951f8bc4a44aed0171ea7fb7a87", "35b618e0f178450fa533aa4cae01470a", "7747f1b60b054a219d02e2a93873b04c", "94976850944d4af7a0e4bc987d9af882", "d086bd77fd194a208a5207b4812e2e92", "5c786e4dd11b46a48fc846612c66181b", "019dfed092d5456186441718c14ed377", "31e18285da0f49c788a42851d5e5d7b3", "86f0ae6918474db69381616b361bac7e", "a55bab394b5a47c0a3ffdab86d51474d", "b324ac810c1b4900ae04345b6bf1dc30", "d3ac9c9ac3ca42b29b58e0ef90fbc322", "42bc0137649247e4b171571298393fd6", "958ad03e991a40b4a82da8cd0b5180f8", "5b3b063f264d4b83871811462ae5f238" ] }, "id": "1JZt0cA3pSa3", "outputId": "5f03db97-605f-4bac-dfc0-d2b70fb23b6e" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='