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"source": [ "os.environ[\"HF_TOKEN\"] = userdata.get('HF_TOKEN')" ], "metadata": { "id": "3XgwC9A4idTY" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Prerequisites\n", "* nf4(4-bit NormalFloat(NF4)) : https://www.kaggle.com/code/lorentzyeung/what-s-4-bit-quantization-how-does-it-help-llama2\n" ], "metadata": { "id": "Cff6aWM0XCCn" } }, { "cell_type": "code", "source": [ "model_id = \"google/gemma-2b\"\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", " bnb_4bit_compute_dtype=torch.bfloat16\n", ")" ], "metadata": { "id": "2B7XLisjijDM" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "tokenizer = AutoTokenizer.from_pretrained(model_id, token=os.environ['HF_TOKEN'])\n", "model = AutoModelForCausalLM.from_pretrained(model_id,\n", " quantization_config=bnb_config,\n", " device_map={\"\":0},\n", " token=os.environ['HF_TOKEN'])" ], "metadata": { "colab": { "base_uri": 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StepTraining Loss
11.680200
20.629800
31.022100
41.030600
50.418500
61.227500
71.092000
80.332100
90.562600
100.479100
110.221400
120.598500
130.540600
140.138400
150.339500
160.275900
170.147900
180.375600
190.348000
200.082300
210.229600
220.211100
230.057200
240.201300
250.173300
260.039700
270.090200
280.070300
290.020200
300.052100
310.044700
320.014400
330.033300
340.022700
350.021300
360.039200
370.039500
380.015100
390.029400
400.021800
410.020800
420.039600
430.038800
440.013100
450.029800
460.028800
470.016500
480.041200
490.039800
500.012600
510.030500
520.028500
530.012900
540.040400
550.040000
560.016300
570.026300
580.027600
590.012500
600.039200
610.039200
620.013600
630.026100
640.027300
650.013100
660.039100
670.039100
680.013700
690.026700
700.026900
710.013100
720.038900
730.038800
740.012900
750.026800
760.026700
770.013200
780.038900
790.038900
800.011800
810.027700
820.025100
830.014400
840.038800
850.038800
860.013400
870.026400
880.025600
890.013900
900.039000
910.039000
920.013600
930.025800
940.025900
950.013200
960.038800
970.038800
980.013200
990.025700
1000.025900

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=100, training_loss=0.1457195576839149, metrics={'train_runtime': 56.7229, 'train_samples_per_second': 7.052, 'train_steps_per_second': 1.763, 'total_flos': 55030401331200.0, 'train_loss': 0.1457195576839149, 'epoch': 66.67})" ] }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "code", "source": [ "text = \"Quote: A woman is like a tea bag;\"\n", "device = \"cuda:0\"\n", "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n", "\n", "outputs = model.generate(**inputs, max_new_tokens=20)\n", "print(tokenizer.decode(outputs[0], skip_special_tokens=True))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "V_RhEzHzcEhZ", "outputId": "cc4105a0-8a4f-4d7b-d0b4-cf546ba71ff7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Quote: A woman is like a tea bag; you can’t tell how strong she is until you put her in hot water\n", "Author: Eleanor\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "ikfZisf2cHMU" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "text = \"Quote: Outside of a dog, a book is man's\"\n", "device = \"cuda:0\"\n", "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n", "\n", "outputs = model.generate(**inputs, max_new_tokens=20)\n", "print(tokenizer.decode(outputs[0], skip_special_tokens=True))" ], "metadata": { "id": "djg3QAMuVx8R", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "31f4ad54-cb1b-4e30-abb5-fa3d6bb3643b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Quote: Outside of a dog, a book is man's best friend.\n", "Author: Nicolas Chamfort\n", "Quote: The most wasted of all days is one\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "TdJIhK-6mrEL" }, "execution_count": null, "outputs": [] } ] }