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alonsosilvaallende/next-token-prediction-liubov.ipynb

Created June 23, 2026 12:33
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/ccd174aaa01b63f5e66d48b70d7ebeda/next-token-prediction-liubov.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IwbSJHskMNnI",
"outputId": "4bed807c-7da3-46b8-e0bf-fe8d3f05be74"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Using device: cuda\n"
]
}
],
"source": [
"import torch\n",
"\n",
"# Auto select device (CUDA > MPS > CPU)\n",
"if torch.cuda.is_available():\n",
" device = torch.device(\"cuda\")\n",
"elif hasattr(torch.backends, \"mps\") and torch.backends.mps.is_available():\n",
" device = torch.device(\"mps\")\n",
"else:\n",
" device = torch.device(\"cpu\")\n",
"print(f\"Using device: {device}\")"
]
},
{
"cell_type": "code",
"source": [
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"\n",
"# MODEL_ID = \"Qwen/Qwen2.5-0.5B-Instruct\"\n",
"MODEL_ID = \"Qwen/Qwen3-1.7B\"\n",
"tokenizer = AutoTokenizer.from_pretrained(\n",
" MODEL_ID,\n",
" cache_dir=\"/content/drive/My Drive/\",\n",
")\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" MODEL_ID,\n",
" cache_dir=\"/content/drive/My Drive/\",\n",
").to(device)"
],
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"id": "x6C7CKnrMVBw",
"outputId": "743b3cf6-6fb8-4296-8283-722d701bb917"
},
"execution_count": 2,
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{
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],
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},
{
"cell_type": "code",
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"openai/gsm8k\", \"main\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 177,
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},
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"README.md: 0%| | 0.00/7.93k [00:00<?, ?B/s]"
],
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"main/train-00000-of-00001.parquet: 0%| | 0.00/2.31M [00:00<?, ?B/s]"
],
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{
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],
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{
"output_type": "display_data",
"data": {
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"Generating train split: 0%| | 0/7473 [00:00<?, ? examples/s]"
],
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{
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"data": {
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],
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]
},
{
"cell_type": "code",
"source": [
"example_question = dataset[\"train\"][\"question\"][1]\n",
"print(example_question)"
],
"metadata": {
"id": "-d47d0qhOBz8",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "343560a3-6ffd-4002-b63d-27d9ff4946e3"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"example_answer = dataset[\"train\"][\"answer\"][1].split(\"####\")[-1]\n",
"example_answer"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "7HG_G6HRbck4",
"outputId": "24602713-7324-44e9-8f1c-04e2ab9da583"
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"' 10'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"messages = [{\"role\": \"user\", \"content\": example_question}]\n",
"prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
"print(prompt)"
],
"metadata": {
"id": "ux2i8lTBOJf-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5276660a-51d8-4af2-eed9-7d890fac7812"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<|im_start|>user\n",
"Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?<|im_end|>\n",
"<|im_start|>assistant\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
"g_cuda = torch.Generator(device=device).manual_seed(42)\n",
"full_response = input_ids.to(device)\n",
"for _ in range(50):\n",
" with torch.no_grad():\n",
" outputs = model(full_response)\n",
" logits = outputs.logits[:, -1, :] # Focus on the last token's logits\n",
" probs = torch.softmax(logits, dim=-1)\n",
" sampled_token = torch.multinomial(probs, num_samples=1, generator=g_cuda)\n",
" if sampled_token in [tokenizer.eos_token_id, tokenizer.pad_token_id]: break\n",
" print(tokenizer.decode(sampled_token[0]), end=\"\")\n",
" full_response = torch.cat([full_response, sampled_token], dim=-1)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PQYGIzpBxKVW",
"outputId": "cd586b5c-02f6-4d7e-850c-4972163399a1"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<think>\n",
"Okay, let's see. Weng earns $12 an hour for babysitting. Yesterday, she did 50 minutes of babysitting. I need to figure out how much she earned. Hmm, so first, I should convert"
]
}
]
},
{
"cell_type": "code",
"source": [
"205 -> 410"
],
"metadata": {
"id": "o71hszQec45F"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Fixed length of thinking tokens"
],
"metadata": {
"id": "i8VGknONxzqE"
}
},
{
"cell_type": "code",
"source": [
"end_of_thinking_token = tokenizer.encode('</think>')[0]\n",
"end_of_thinking_token"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9YYkV9NqPS-C",
"outputId": "f607856c-49d3-4f63-f422-d221d70ea6e6"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"151668"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"forced_tokens = tokenizer.encode(\"\\nFinal answer: \\\\boxed{\")\n",
"forced_tokens"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ENO_03zpW2wd",
"outputId": "46c7a72f-4012-4d9f-ad08-0a9267f1ba26"
},
"execution_count": 40,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[198, 19357, 4226, 25, 1124, 79075, 90]"
]
},
"metadata": {},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"source": [
"number_of_thinking_tokens = 50\n",
"total_number_of_tokens = number_of_thinking_tokens + 20"
],
"metadata": {
"id": "rHeZwWrhPiEW"
},
"execution_count": 43,
"outputs": []
},
{
"cell_type": "code",
"source": [
"g_cuda = torch.Generator(device=device).manual_seed(42)\n",
"input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
"full_response = input_ids.to(device)\n",
"for token_number in range(total_number_of_tokens):\n",
" with torch.no_grad():\n",
" outputs = model(full_response)\n",
" logits = outputs.logits[:, -1, :] # Focus on the last token's logits\n",
" neg_inf = torch.tensor(-torch.inf, device=logits.device, dtype=logits.dtype)\n",
" if token_number < number_of_thinking_tokens:\n",
" allowed = torch.ones(len(logits[0]), dtype=torch.bool, device=device)\n",
" allowed[end_of_thinking_token] = False\n",
" logits = torch.where(allowed, logits, neg_inf)\n",
" elif token_number == number_of_thinking_tokens:\n",
" allowed = torch.zeros(len(logits[0]), dtype=torch.bool, device=device)\n",
" allowed[end_of_thinking_token] = True\n",
" logits = torch.where(allowed, logits, neg_inf)\n",
" elif 1 <= token_number - number_of_thinking_tokens <= len(forced_tokens):\n",
" forced_index = token_number - number_of_thinking_tokens - 1\n",
" allowed = torch.zeros(len(logits[0]), dtype=torch.bool, device=device)\n",
" allowed[forced_tokens[forced_index]] = True\n",
" logits = torch.where(allowed, logits, neg_inf)\n",
" probs = torch.softmax(logits, dim=-1)\n",
" sampled_token = torch.multinomial(probs, num_samples=1, generator=g_cuda)\n",
" if sampled_token in [tokenizer.eos_token_id, tokenizer.pad_token_id]: break\n",
" print(tokenizer.decode(sampled_token[0]), end=\"\")\n",
" full_response = torch.cat([full_response, sampled_token], dim=-1)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EFVWYQk6Ozyp",
"outputId": "9c5dc3ac-feec-4279-c023-b765c8900a10"
},
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<think>\n",
"Okay, let's see. Weng earns $12 an hour for babysitting. Yesterday, she did 50 minutes of babysitting. I need to figure out how much she earned. Hmm, so first, I should convert</think>\n",
"Final answer: \\boxed{10}\n",
"\n",
"Wait, let me check that again. So"
]
}
]
},
{
"cell_type": "code",
"source": [
"pred_answer = tokenizer.decode(full_response[0]).split(\"boxed{\")[1].split('}')[0]\n",
"pred_answer"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "SN83rDchzP7u",
"outputId": "a018d445-b05a-4307-f945-2005e479543c"
},
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'10'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [
"int(example_answer) == int(pred_answer)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AhZK01vSzo3q",
"outputId": "f1a99c8b-c280-4972-843d-6f51567fa988"
},
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"source": [
"start_of_thinking_token = tokenizer.encode(\"<think>\")[0]\n",
"start_of_thinking_token"
],
"metadata": {
"id": "uPXrYaQFfgac",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a079ceed-195f-4114-a426-52a5a8a59060"
},
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"151667"
]
},
"metadata": {},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"source": [
"index_start = next((i for i, val in enumerate(full_response[0].tolist()) if val == start_of_thinking_token), -1)\n",
"index_start"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "07mtTLvqfrKq",
"outputId": "92c082e4-f997-465e-9276-1559c0e6a41a"
},
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"39"
]
},
"metadata": {},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"source": [
"index_end = next((i for i, val in enumerate(full_response[0].tolist()) if val == end_of_thinking_token), -1)\n",
"index_end"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6oJ5q4oXywjj",
"outputId": "66181305-5ffe-48e2-fda4-9d0124c79b13"
},
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"239"
]
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"source": [
"print(tokenizer.decode(full_response[0][index_start:index_end+1]))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JvhyHmrpf-YV",
"outputId": "ec2fc7db-8b39-4f50-ea06-8124ccc67a15"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<think>\n",
"Okay, let's see. Weng earns $12 an hour for babysitting. Yesterday, she did 50 minutes of babysitting. I need to figure out how much she earned. Hmm, so first, I should convert the time she worked from minutes to hours because her pay rate is given per hour. \n",
"\n",
"Right, 50 minutes is how many hours? Well, there are 60 minutes in an hour. So if I divide 50 by 60, that should give me the fraction of an hour she worked. Let me do that calculation. 50 divided by 60. Hmm, 50 divided by 60 is the same as 5/6, which is approximately 0.8333 hours. Wait, is that right? Let me check. 60 minutes in an hour, so 50 minutes is 50/60 hours. Yes, that's correct. \n",
"\n",
"</think>\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "l9UXKdxzy7An"
},
"execution_count": null,
"outputs": []
}
]
}
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