project_bookworm/Local_CPU_LLM_Bling_Non_Interactive.ipynb

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   "source": [
    "# Use a local CPU Large Language Model (LLM) to generate text\n",
    "\n",
    "This is a basic LLM, which \n",
    "\n",
    "* does not require a GPU\n",
    "* is not fine-tuned for a specific task\n",
    "* is not optimized for speed\n",
    "* is not optimized for memory usage\n",
    "* has a smaller model size\n",
    "* ...\n",
    "* is not as good as a GPU LLM\n",
    "* is not as good as a fine-tuned LLM\n",
    "* is not as good as a larger LLM\n",
    "* ...\n",
    "\n",
    "Its purpose is to allow on-premises and self-hosted use of LLMs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "initial_id",
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   "source": [
    "# You need to manage the dependencies of LangChain with\n",
    "# the requirements.txt file. The versions are pinned.\n",
    "# %pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96a287c1fc724d2",
   "metadata": {
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   "source": [
    "## Use the Hugging Face pipeline with LLMware Bling\n",
    "\n",
    "* The Hugging Face pipeline is a convenient way to use a pre-trained model.\n",
    "* LLMware Bling is a CPU LLM.\n",
    "* The config of this model is to allow remote code from Hugging Face."
   ]
  },
  {
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   "execution_count": 2,
   "id": "2108b1c9373e0ec8",
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     "start_time": "2024-03-17T11:22:45.974223Z"
    },
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading file vocab.json from cache at None\n",
      "loading file merges.txt from cache at None\n",
      "loading file tokenizer.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/tokenizer.json\n",
      "loading file added_tokens.json from cache at None\n",
      "loading file special_tokens_map.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/special_tokens_map.json\n",
      "loading file tokenizer_config.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/tokenizer_config.json\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
      "loading configuration file config.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/config.json\n",
      "loading configuration file config.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/config.json\n",
      "Model config StableLMEpochConfig {\n",
      "  \"_name_or_path\": \"llmware/bling-stable-lm-3b-4e1t-v0\",\n",
      "  \"architectures\": [\n",
      "    \"StableLMEpochForCausalLM\"\n",
      "  ],\n",
      "  \"auto_map\": {\n",
      "    \"AutoConfig\": \"llmware/bling-stable-lm-3b-4e1t-v0--configuration_stablelm_epoch.StableLMEpochConfig\",\n",
      "    \"AutoModelForCausalLM\": \"llmware/bling-stable-lm-3b-4e1t-v0--modeling_stablelm_epoch.StableLMEpochForCausalLM\"\n",
      "  },\n",
      "  \"bos_token_id\": 0,\n",
      "  \"eos_token_id\": 0,\n",
      "  \"hidden_act\": \"silu\",\n",
      "  \"hidden_size\": 2560,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 6912,\n",
      "  \"max_position_embeddings\": 4096,\n",
      "  \"model_type\": \"stablelm_epoch\",\n",
      "  \"norm_eps\": 1e-05,\n",
      "  \"num_attention_heads\": 32,\n",
      "  \"num_heads\": 32,\n",
      "  \"num_hidden_layers\": 32,\n",
      "  \"num_key_value_heads\": 32,\n",
      "  \"rope_pct\": 0.25,\n",
      "  \"rope_theta\": 10000,\n",
      "  \"rotary_scaling_factor\": 1.0,\n",
      "  \"tie_word_embeddings\": false,\n",
      "  \"torch_dtype\": \"bfloat16\",\n",
      "  \"transformers_version\": \"4.38.2\",\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 50304\n",
      "}\n",
      "\n",
      "loading weights file pytorch_model.bin from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/pytorch_model.bin\n",
      "Generate config GenerationConfig {\n",
      "  \"bos_token_id\": 0,\n",
      "  \"eos_token_id\": 0\n",
      "}\n",
      "\n",
      "All model checkpoint weights were used when initializing StableLMEpochForCausalLM.\n",
      "\n",
      "All the weights of StableLMEpochForCausalLM were initialized from the model checkpoint at llmware/bling-stable-lm-3b-4e1t-v0.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use StableLMEpochForCausalLM for predictions without further training.\n",
      "loading configuration file generation_config.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/generation_config.json\n",
      "Generate config GenerationConfig {\n",
      "  \"bos_token_id\": 0,\n",
      "  \"eos_token_id\": 0\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
    "\n",
    "model_id = \"llmware/bling-stable-lm-3b-4e1t-v0\"\n",
    "\n",
    "# Ensure the directory for saving models is created and specified in your environment\n",
    "# This is more about ensuring that the model download doesn't prompt for storage location or confirmation\n",
    "import os\n",
    "from transformers import logging\n",
    "\n",
    "# Optionally, increase logging level if you want to see more details about the download process\n",
    "logging.set_verbosity_info()\n",
    "\n",
    "# Make sure you have set TRANSFORMERS_CACHE in your environment variables\n",
    "# os.environ[\"TRANSFORMERS_CACHE\"] = \"/path/to/your/preferred/cache/directory\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)\n",
    "\n",
    "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=500)\n",
    "hf = HuggingFacePipeline(pipeline=pipe)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "904e6bf72c2ecf27",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Use the Hugging Face pipeline with LLMware Bling via LangChain\n",
    "\n",
    "* This is a basic prompt template with LangChain\n",
    "* The question is passed to the model via a chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1827b8c3423066b0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-17T11:23:25.839334Z",
     "start_time": "2024-03-17T11:23:02.070024Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Disabling tokenizer parallelism, we're using DataLoader multithreading already\n",
      "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
     ]
    }
   ],
   "source": [
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "prompt = PromptTemplate.from_template(template)\n",
    "\n",
    "chain = prompt | hf\n",
    "\n",
    "question = \"What is electroencephalography?\"\n",
    "\n",
    "test = chain.invoke({\"question\": question})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac2a19b6fb9aa3e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-17T11:23:25.847308Z",
     "start_time": "2024-03-17T11:23:25.841002Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " First, electroencephalography (EEG) is a medical test that measures electrical activity in the brain. Second, EEG is a type of electrodiagnostic test. Third, electrodiagnostic tests are used to evaluate neurological conditions.\n"
     ]
    }
   ],
   "source": [
    "print(test)"
   ]
  }
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