{
"cells": [
{
"cell_type": "code",
"source": [
"# Reference: https://unsloth.ai/blog/r1-reasoning"
],
"metadata": {
"id": "kY8jzinCqlzF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TISCz0mCsmNo"
},
"outputs": [],
"source": [
"!pip install unsloth vllm\n",
"!pip install --upgrade pillow\n",
"!pip install git+https://github.com/huggingface/trl.git@e95f9fb74a3c3647b86f251b7e230ec51c64b72b"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "59DIs5BMcvjN",
"outputId": "462c6f34-4324-4d88-bb9c-e9e55c720306"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: Patching Xformers to fix some performance issues.\n",
"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
"🦥 Unsloth Zoo will now patch everything to make training faster!\n",
"INFO 02-07 18:05:13 __init__.py:190] Automatically detected platform cuda.\n"
]
}
],
"source": [
"from unsloth import FastLanguageModel, PatchFastRL\n",
"PatchFastRL(\"GRPO\", FastLanguageModel)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 930,
"referenced_widgets": [
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},
"id": "DkIvEkIIkEyB",
"outputId": "185e9dd2-6428-44d7-b78c-d7b5d7cb42e6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"==((====))== Unsloth 2025.2.4: Fast Llama patching. Transformers: 4.48.2.\n",
" \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n",
"O^O/ \\_/ \\ Torch: 2.5.1+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.1.0\n",
"\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
" \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n",
"Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
"Unsloth: vLLM loading unsloth/meta-llama-3.1-8b-instruct-bnb-4bit with actual GPU utilization = 59.59%\n",
"Unsloth: Your GPU has CUDA compute capability 7.5 with VRAM = 14.74 GB.\n",
"Unsloth: Using conservativeness = 1.0. Chunked prefill tokens = 512. Num Sequences = 160.\n",
"Unsloth: vLLM's KV Cache can use up to 2.61 GB. Also swap space = 2 GB.\n",
"WARNING 02-07 18:05:47 config.py:2386] Casting torch.bfloat16 to torch.float16.\n",
"INFO 02-07 18:06:00 config.py:542] This model supports multiple tasks: {'generate', 'classify', 'embed', 'reward', 'score'}. Defaulting to 'generate'.\n",
"Unsloth: vLLM Bitsandbytes config using kwargs = {'load_in_8bit': False, 'load_in_4bit': True, 'bnb_4bit_compute_dtype': 'float16', 'bnb_4bit_quant_storage': 'uint8', 'bnb_4bit_quant_type': 'nf4', 'bnb_4bit_use_double_quant': True, 'llm_int8_enable_fp32_cpu_offload': False, 'llm_int8_has_fp16_weight': False, 'llm_int8_skip_modules': ['lm_head', 'multi_modal_projector', 'merger', 'modality_projection'], 'llm_int8_threshold': 6.0}\n",
"INFO 02-07 18:06:00 llm_engine.py:234] Initializing a V0 LLM engine (v0.7.2) with config: model='unsloth/meta-llama-3.1-8b-instruct-bnb-4bit', speculative_config=None, tokenizer='unsloth/meta-llama-3.1-8b-instruct-bnb-4bit', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=512, download_dir=None, load_format=LoadFormat.BITSANDBYTES, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=bitsandbytes, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='xgrammar'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=unsloth/meta-llama-3.1-8b-instruct-bnb-4bit, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=True, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={\"level\":0,\"splitting_ops\":[],\"compile_sizes\":[],\"cudagraph_capture_sizes\":[160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],\"max_capture_size\":160}, use_cached_outputs=False, \n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/55.5k [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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},
{
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],
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"version_major": 2,
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}
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"generation_config.json: 0%| | 0.00/239 [00:00, ?B/s]"
],
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"version_major": 2,
"version_minor": 0,
"model_id": "76dbac87ea1e4d79afe609d25b693d52"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"INFO 02-07 18:06:03 cuda.py:179] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.\n",
"INFO 02-07 18:06:03 cuda.py:227] Using XFormers backend.\n",
"INFO 02-07 18:06:04 model_runner.py:1110] Starting to load model unsloth/meta-llama-3.1-8b-instruct-bnb-4bit...\n",
"INFO 02-07 18:06:04 loader.py:1102] Loading weights with BitsAndBytes quantization. May take a while ...\n",
"INFO 02-07 18:06:05 weight_utils.py:252] Using model weights format ['*.safetensors']\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/5.70G [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ef3a81e813d844b887537a3ac5d5005b"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00, ?it/s]\n"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "4cc4d99b961b4d74a662b736e325add2"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00, ?it/s]\n"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "bf5af084b7524cf3818592777ecfedf9"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"INFO 02-07 18:08:10 model_runner.py:1115] Loading model weights took 5.3541 GB\n",
"INFO 02-07 18:08:10 punica_selector.py:18] Using PunicaWrapperGPU.\n",
"INFO 02-07 18:08:26 worker.py:267] Memory profiling takes 14.63 seconds\n",
"INFO 02-07 18:08:26 worker.py:267] the current vLLM instance can use total_gpu_memory (14.74GiB) x gpu_memory_utilization (0.60) = 8.78GiB\n",
"INFO 02-07 18:08:26 worker.py:267] model weights take 5.35GiB; non_torch_memory takes 0.05GiB; PyTorch activation peak memory takes 0.74GiB; the rest of the memory reserved for KV Cache is 2.64GiB.\n",
"INFO 02-07 18:08:26 executor_base.py:110] # CUDA blocks: 1353, # CPU blocks: 1024\n",
"INFO 02-07 18:08:26 executor_base.py:115] Maximum concurrency for 512 tokens per request: 42.28x\n",
"INFO 02-07 18:08:28 model_runner.py:1434] Capturing cudagraphs for decoding. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI. If out-of-memory error occurs during cudagraph capture, consider decreasing `gpu_memory_utilization` or switching to eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Capturing CUDA graph shapes: 100%|██████████| 23/23 [00:38<00:00, 1.69s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"INFO 02-07 18:09:07 model_runner.py:1562] Graph capturing finished in 39 secs, took 0.58 GiB\n",
"INFO 02-07 18:09:07 llm_engine.py:431] init engine (profile, create kv cache, warmup model) took 56.64 seconds\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/55.5k [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "59ff091ac493435c89b4d413703ccf11"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2daa1ed02aac48949ddb3b8bae31b901"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/454 [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e19f37e488334e40833ffae132deb313"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Not an error, but Unsloth cannot patch MLP layers with our manual autograd engine since either LoRA adapters\n",
"are not enabled or a bias term (like in Qwen) is used.\n",
"Unsloth 2025.2.4 patched 32 layers with 32 QKV layers, 32 O layers and 0 MLP layers.\n"
]
}
],
"source": [
"from unsloth import is_bfloat16_supported\n",
"import torch\n",
"max_seq_length = 512\n",
"lora_rank = 8\n",
"\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"meta-llama/meta-Llama-3.1-8B-Instruct\",\n",
" max_seq_length = max_seq_length,\n",
" load_in_4bit = True,\n",
" fast_inference = True,\n",
" max_lora_rank = lora_rank,\n",
" gpu_memory_utilization = 0.6,\n",
")\n",
"\n",
"model = FastLanguageModel.get_peft_model(\n",
" model,\n",
" r = lora_rank,\n",
" target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",],\n",
" lora_alpha = lora_rank,\n",
" use_gradient_checkpointing = \"unsloth\",\n",
")"
]
},
{
"cell_type": "code",
"source": [
"from datasets import load_dataset\n",
"import re\n",
"\n",
"SYSTEM_PROMPT = \"\"\"\n",
"Respond in the following format:\n",
"
| Step | \n", "Training Loss | \n", "reward | \n", "reward_std | \n", "completion_length | \n", "kl | \n", "
|---|---|---|---|---|---|
| 1 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "196.500000 | \n", "0.000000 | \n", "
| 2 | \n", "0.000000 | \n", "0.040667 | \n", "0.099613 | \n", "183.500000 | \n", "0.000000 | \n", "
| 3 | \n", "0.000000 | \n", "-0.019833 | \n", "0.035000 | \n", "137.000000 | \n", "0.000005 | \n", "
| 4 | \n", "0.000000 | \n", "0.166667 | \n", "0.258199 | \n", "189.166672 | \n", "0.000006 | \n", "
| 5 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "192.500000 | \n", "0.000004 | \n", "
| 6 | \n", "0.000000 | \n", "0.029333 | \n", "0.050274 | \n", "144.833344 | \n", "0.000005 | \n", "
| 7 | \n", "0.000000 | \n", "0.367833 | \n", "1.051099 | \n", "159.333344 | \n", "0.000007 | \n", "
| 8 | \n", "0.000000 | \n", "0.777667 | \n", "1.157384 | \n", "171.833344 | \n", "0.000007 | \n", "
| 9 | \n", "0.000000 | \n", "-0.034000 | \n", "0.083283 | \n", "149.500000 | \n", "0.000008 | \n", "
| 10 | \n", "0.000000 | \n", "0.447167 | \n", "1.008919 | \n", "94.333336 | \n", "0.000010 | \n", "
| 11 | \n", "0.000000 | \n", "0.083833 | \n", "0.102552 | \n", "110.666672 | \n", "0.000009 | \n", "
| 12 | \n", "0.000000 | \n", "-0.093333 | \n", "0.228619 | \n", "168.500000 | \n", "0.000005 | \n", "
| 13 | \n", "0.000000 | \n", "0.017667 | \n", "0.043274 | \n", "137.166672 | \n", "0.000005 | \n", "
| 14 | \n", "0.000000 | \n", "-0.022333 | \n", "0.054705 | \n", "147.333344 | \n", "0.000023 | \n", "
| 15 | \n", "0.000000 | \n", "-0.039000 | \n", "0.095530 | \n", "197.000000 | \n", "0.000009 | \n", "
| 16 | \n", "0.000000 | \n", "0.416667 | \n", "1.020621 | \n", "153.666672 | \n", "0.000007 | \n", "
| 17 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "200.000000 | \n", "0.000005 | \n", "
| 18 | \n", "0.000000 | \n", "-0.149667 | \n", "0.233040 | \n", "194.333344 | \n", "0.000016 | \n", "
| 19 | \n", "0.000000 | \n", "0.382500 | \n", "0.878986 | \n", "170.500000 | \n", "0.000016 | \n", "
| 20 | \n", "0.000000 | \n", "-0.070667 | \n", "0.111448 | \n", "169.166672 | \n", "0.000010 | \n", "
| 21 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "166.666672 | \n", "0.000011 | \n", "
| 22 | \n", "0.000000 | \n", "0.020833 | \n", "0.051031 | \n", "190.666672 | \n", "0.000041 | \n", "
| 23 | \n", "0.000000 | \n", "0.405333 | \n", "0.992860 | \n", "130.000000 | \n", "0.000046 | \n", "
| 24 | \n", "0.000000 | \n", "0.415500 | \n", "1.017763 | \n", "102.833336 | \n", "0.000078 | \n", "
| 25 | \n", "0.000000 | \n", "0.387667 | \n", "1.026366 | \n", "135.500000 | \n", "0.000072 | \n", "
| 26 | \n", "0.000000 | \n", "0.404167 | \n", "0.990002 | \n", "155.666672 | \n", "0.000092 | \n", "
| 27 | \n", "0.000000 | \n", "-0.031167 | \n", "0.076342 | \n", "177.166672 | \n", "0.000038 | \n", "
| 28 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "131.666672 | \n", "0.000068 | \n", "
| 29 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "200.000000 | \n", "0.000017 | \n", "
| 30 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "177.000000 | \n", "0.000052 | \n", "
| 31 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "181.333344 | \n", "0.000014 | \n", "
| 32 | \n", "0.000000 | \n", "0.370167 | \n", "0.906719 | \n", "174.000000 | \n", "0.000042 | \n", "
| 33 | \n", "0.000000 | \n", "0.416667 | \n", "1.020621 | \n", "119.666672 | \n", "0.000278 | \n", "
| 34 | \n", "0.000000 | \n", "0.379500 | \n", "0.929581 | \n", "190.166672 | \n", "0.000037 | \n", "
| 35 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "184.500000 | \n", "0.000106 | \n", "
| 36 | \n", "0.000000 | \n", "0.083333 | \n", "0.204124 | \n", "194.166672 | \n", "0.000094 | \n", "
| 37 | \n", "0.000000 | \n", "0.835667 | \n", "1.234232 | \n", "108.833336 | \n", "0.000893 | \n", "
| 38 | \n", "0.000000 | \n", "0.432167 | \n", "1.013710 | \n", "117.000000 | \n", "0.000292 | \n", "
| 39 | \n", "0.000000 | \n", "0.416667 | \n", "1.020621 | \n", "129.166672 | \n", "0.000199 | \n", "
| 40 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "154.833344 | \n", "0.000118 | \n", "
| 41 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "158.166672 | \n", "0.000172 | \n", "
| 42 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "187.500000 | \n", "0.000084 | \n", "
| 43 | \n", "0.000000 | \n", "-0.033167 | \n", "0.151592 | \n", "188.166672 | \n", "0.000254 | \n", "
| 44 | \n", "0.000000 | \n", "-0.053833 | \n", "0.131864 | \n", "176.166672 | \n", "0.000330 | \n", "
| 45 | \n", "0.000000 | \n", "0.416667 | \n", "1.020621 | \n", "178.166672 | \n", "0.000246 | \n", "
| 46 | \n", "0.000000 | \n", "0.028333 | \n", "0.069402 | \n", "123.333336 | \n", "0.000267 | \n", "
| 47 | \n", "0.000000 | \n", "0.389000 | \n", "0.952852 | \n", "172.833344 | \n", "0.000389 | \n", "
| 48 | \n", "0.000000 | \n", "0.032000 | \n", "0.053081 | \n", "147.666672 | \n", "0.000485 | \n", "
| 49 | \n", "0.000100 | \n", "0.397000 | \n", "0.972448 | \n", "141.666672 | \n", "0.001697 | \n", "
| 50 | \n", "0.000100 | \n", "0.343500 | \n", "1.007951 | \n", "170.500000 | \n", "0.001275 | \n", "
| 51 | \n", "0.000000 | \n", "0.416667 | \n", "1.020621 | \n", "95.666672 | \n", "0.000896 | \n", "
| 52 | \n", "0.000000 | \n", "0.390333 | \n", "0.956118 | \n", "130.333344 | \n", "0.000787 | \n", "
| 53 | \n", "0.000100 | \n", "2.083333 | \n", "1.020621 | \n", "112.166672 | \n", "0.002470 | \n", "
| 54 | \n", "0.000100 | \n", "2.083333 | \n", "1.020621 | \n", "159.333344 | \n", "0.002564 | \n", "
| 55 | \n", "0.000000 | \n", "0.025667 | \n", "0.062870 | \n", "167.333344 | \n", "0.000523 | \n", "
| 56 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "200.000000 | \n", "0.000134 | \n", "
| 57 | \n", "0.000100 | \n", "0.716833 | \n", "1.246680 | \n", "128.166672 | \n", "0.001942 | \n", "
| 58 | \n", "0.000200 | \n", "0.125333 | \n", "0.209406 | \n", "150.666672 | \n", "0.003995 | \n", "
| 59 | \n", "0.000100 | \n", "-0.070000 | \n", "0.171464 | \n", "161.500000 | \n", "0.002853 | \n", "
| 60 | \n", "0.000100 | \n", "-0.059333 | \n", "0.145336 | \n", "167.833344 | \n", "0.002419 | \n", "
| 61 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "187.000000 | \n", "0.000462 | \n", "
| 62 | \n", "0.000000 | \n", "0.351500 | \n", "0.860996 | \n", "179.000000 | \n", "0.000890 | \n", "
| 63 | \n", "0.000100 | \n", "0.378167 | \n", "0.938122 | \n", "142.000000 | \n", "0.002276 | \n", "
| 64 | \n", "0.000300 | \n", "-0.019667 | \n", "0.120294 | \n", "178.333344 | \n", "0.006654 | \n", "
| 65 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "200.000000 | \n", "0.000166 | \n", "
| 66 | \n", "0.000100 | \n", "1.006000 | \n", "1.207874 | \n", "150.166672 | \n", "0.003213 | \n", "
| 67 | \n", "0.000200 | \n", "0.176333 | \n", "0.214932 | \n", "130.166672 | \n", "0.004481 | \n", "
| 68 | \n", "0.000100 | \n", "0.000000 | \n", "0.000000 | \n", "169.166672 | \n", "0.001862 | \n", "
| 69 | \n", "0.000300 | \n", "-0.055000 | \n", "0.106113 | \n", "115.666672 | \n", "0.007103 | \n", "
| 70 | \n", "0.000000 | \n", "0.416667 | \n", "1.020621 | \n", "160.500000 | \n", "0.000806 | \n", "
| 71 | \n", "0.000100 | \n", "0.394167 | \n", "1.039329 | \n", "193.500000 | \n", "0.003651 | \n", "
| 72 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "182.333344 | \n", "0.000763 | \n", "
| 73 | \n", "0.000200 | \n", "0.083333 | \n", "0.204124 | \n", "192.166672 | \n", "0.004004 | \n", "
| 74 | \n", "0.000100 | \n", "1.281500 | \n", "1.289369 | \n", "171.333344 | \n", "0.002141 | \n", "
| 75 | \n", "0.000200 | \n", "0.861667 | \n", "1.162246 | \n", "148.500000 | \n", "0.005056 | \n", "
| 76 | \n", "0.000400 | \n", "0.021000 | \n", "0.051439 | \n", "110.500000 | \n", "0.008968 | \n", "
| 77 | \n", "0.000200 | \n", "0.387667 | \n", "1.037166 | \n", "117.500000 | \n", "0.004921 | \n", "
| 78 | \n", "0.000200 | \n", "2.434667 | \n", "0.078004 | \n", "139.000000 | \n", "0.005271 | \n", "
| 79 | \n", "0.000200 | \n", "-0.041333 | \n", "0.101246 | \n", "114.666672 | \n", "0.005558 | \n", "
| 80 | \n", "0.000100 | \n", "0.323333 | \n", "0.919944 | \n", "183.666672 | \n", "0.001545 | \n", "
| 81 | \n", "0.000300 | \n", "0.742667 | \n", "1.294775 | \n", "183.166672 | \n", "0.006558 | \n", "
| 82 | \n", "0.000200 | \n", "1.250000 | \n", "1.369306 | \n", "127.166672 | \n", "0.005109 | \n", "
| 83 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "173.333344 | \n", "0.001094 | \n", "
| 84 | \n", "0.000200 | \n", "1.658333 | \n", "1.238260 | \n", "131.333344 | \n", "0.005867 | \n", "
| 85 | \n", "0.000100 | \n", "0.000000 | \n", "0.000000 | \n", "133.833344 | \n", "0.001652 | \n", "
| 86 | \n", "0.000100 | \n", "0.000000 | \n", "0.000000 | \n", "182.666672 | \n", "0.002227 | \n", "
| 87 | \n", "0.000400 | \n", "1.633333 | \n", "1.267544 | \n", "118.333336 | \n", "0.009755 | \n", "
| 88 | \n", "0.000200 | \n", "0.326167 | \n", "0.915721 | \n", "189.833344 | \n", "0.004607 | \n", "
| 89 | \n", "0.000100 | \n", "0.416667 | \n", "1.020621 | \n", "186.833344 | \n", "0.002445 | \n", "
"
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"text": [
"-------------------- Question:\n",
"Edith is a receptionist at a local office and is organizing files into cabinets. She had 60 files and finished organizing half of them this morning. She has another 15 files to organize in the afternoon and the rest of the files are missing. How many files are missing? \n",
"Answer:\n",
"15 \n",
"Response:\n",
"To find out how many files are missing, we first need to know how many files Edith organized in the morning and how many she has to organize in the afternoon.\n",
"\n",
"She organized half of the 60 files in the morning. To find half, we divide 60 by 2.\n",
"\n",
"60 / 2 = 30\n",
"\n",
"So, Edith organized 30 files in the morning.\n",
"\n",
"In the afternoon, she has 15 files to organize. \n",
"\n",
"The total number of files she organized or has to organize is 30 + 15 = 45.\n",
"\n",
"Since she had 60 files initially, the number of missing files can be found by subtracting the total number of organized and unorganized files from the initial total.\n",
"\n",
"60 - 45 = 15 \n",
"\n",
"There are 15 files missing. \n",
"Extracted:\n",
"To find out how many files are missing, we first need to know how many files Edith organized in the morning and how many she has to organize in the afternoon.\n",
"\n",
"She organized half of the 60 files in the morning. To find half, we divide 60 by 2.\n",
"\n",
"60 / 2 = 30\n",
"\n",
"So, Edith organized 30 files in the morning.\n",
"\n",
"In the afternoon, she has 15 files to organize. \n",
"\n",
"The total number of files she organized or has to organize is 30 + 15 = 45.\n",
"\n",
"Since she had 60 files initially, the number of missing files can be found by subtracting the total number of organized and unorganized files from the initial total.\n",
"\n",
"60 - 45 = 15 \n",
"\n",
"There are 15 files missing.\n"
]
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"ename": "KeyboardInterrupt",
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\n",
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"