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- "start_time": "2024-07-01T11:08:00.067738Z"
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- }
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- },
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- "source": [
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- "import torch\n",
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- "from transformers import AutoTokenizer, AutoModelForCausalLM,TrainingArguments\n",
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- "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
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- ],
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- "outputs": [],
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- }
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- },
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- "source": [
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- "model = AutoModelForCausalLM.from_pretrained(\"meta-llama/Llama-2-7b-hf\", device_map=\"auto\", torch_dtype = \"auto\",cache_dir=\"D:/a\" )\n",
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- "tokenizer = AutoTokenizer.from_pretrained(\"meta-llama/Llama-2-7b-hf\", cache_dir=\"D:/a\", use_fast=True)"
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- ],
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- "outputs": [
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- "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
 
 
 
 
 
 
 
 
 
 
 
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  },
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  {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "WARNING:root:Some parameters are on the meta device device because they were offloaded to the disk.\n"
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- ]
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- }
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- }
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- },
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- "source": [
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- "def get_llama2_response(prompt, max_new_tokens=50):\n",
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- " inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
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- " outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature= 0.00001)\n",
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- " response = tokenizer.decode(outputs(0), skip_special_tokens=True)\n",
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- " return response"
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- ],
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- "outputs": [],
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- },
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- {
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- "metadata": {},
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- "outputs": [
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- "text/plain": [
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- "'Q:how to find the llama A:The llama is a South American camelid, a domesticated species of the genus Lama. It is used for'"
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- ]
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- },
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  }
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- "prompt = \"Q:how to find the llama A:\"\n",
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- "get_llama2_response(prompt, max_new_tokens=25)"
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  }
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- },
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- "prompt = \"Q:SQL query to extract data from Employee Table A:\"\n",
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- "get_llama2_response(prompt, max_new_tokens=25)"
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- ],
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+ "import torch\n",
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+ "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments\n",
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+ },
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+ "source": [
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+ "tokenizer = AutoTokenizer.from_pretrained(modelName)\n",
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+ "model = AutoModelForSeq2SeqLM.from_pretrained(modelName).to(device)"
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+ ],
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stderr",
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+ "text": [
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+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
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+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
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+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
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+ "You will be able to reuse this secret in all of your notebooks.\n",
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+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
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+ " warnings.warn(\n",
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+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
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+ " warnings.warn(\n"
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+ ]
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+ },
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+ "source": [
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+ "def get_llama2_response(prompt, max_new_tokens=100):\n",
198
+ " inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
199
+ " outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature= 1)\n",
200
+ " response = tokenizer.decode(outputs[0], skip_special_tokens=True ,clean_up_tokenization_spaces=True) # Use indexing instead of calling\n",
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+ " return response"
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+ ],
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+ "outputs": [],
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+ "execution_count": 7
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