Package: languagemodels
Language ModelsPython building blocks to explore large language models in as little as 512MB of RAMThis package makes using large language models from Python as simple as possible. All inference is performed locally to keep your data private by default.Installation and Getting StartedThis package can be installed using the following command:pipinstalllanguagemodelsOnce installed, you should be able to interact with the package in Python as follows:>>>importlanguagemodelsaslm>>>lm.do("What color is the sky?")'The color of the sky is blue.'This will require downloading a significant amount of data (~250MB) on the first run. Models will be cached for later use and subsequent calls should be quick.Example UsageHere are some usage examples as Python REPL sessions. This should work in the REPL, notebooks, or in traditional scripts and applications.Instruction Following>>>importlanguagemodelsaslm>>>lm.do("Translate to English: Hola, mundo!")'Hello, world!'>>>lm.do("What is the capital of France?")'Paris.'Outputs can be restricted to a list of choices if desired:>>>lm.do("Is Mars larger than Saturn?",choices=["Yes","No"])'No'Adjusting Model PerformanceThe base model should run quickly on any system with 512MB of memory, but this memory limit can be increased to select more powerful models that will consume more resources. Here's an example:>>>importlanguagemodelsaslm>>>lm.do("If I have 7 apples then eat 5, how many apples do I have?")'You have 8 apples.'>>>lm.config["max_ram"]="4gb"4.0>>>lm.do("If I have 7 apples then eat 5, how many apples do I have?")'I have 2 apples left.'GPU AccelerationIf you have an NVIDIA GPU with CUDA available, you can opt in to using the GPU for inference:>>>importlanguagemodelsaslm>>>lm.config["device"]="auto"Text Completions>>>importlanguagemodelsaslm>>>lm.complete("She hid in her room until")'she was sure she was safe'Chat>>>lm.chat('''...      System: Respond as a helpful assistant.......      User: What time is it?......      Assistant:...      ''')'I'msorry,butasanAIlanguagemodel,Idon't have access to real-time information. Please provide me with the specific time you are asking for so that I can assist you better.'CodeA model tuned on Python code is included. It can be used to complete code snippets.>>>importlanguagemodelsaslm>>>lm.code("""... a = 2... b = 5...... # Swap a and b... """)'a, b = b, a'External RetrievalHelper functions are provided to retrieve text from external sources that can be used to augment prompt context.>>>importlanguagemodelsaslm>>>lm.get_wiki('Chemistry')'Chemistry is the scientific study...>>>lm.get_weather(41.8,-87.6)'Partly cloudy with a chance of rain...>>>lm.get_date()'Friday, May 12, 2023 at 09:27AM'Here's an example showing how this can be used (compare to previous chat example):>>>lm.chat(f'''...      System: Respond as a helpful assistant. It is{lm.get_date()}......      User: What time is it?......      Assistant:...      ''')'It is currently Wednesday, June 07, 2023 at 12:53PM.'Semantic SearchSemantic search is provided to retrieve documents that may provide helpful context from a document store.>>>importlanguagemodelsaslm>>>lm.store_doc(lm.get_wiki("Python"),"Python")>>>lm.store_doc(lm.get_wiki("C language"),"C")>>>lm.store_doc(lm.get_wiki("Javascript"),"Javascript")>>>lm.get_doc_context("What does it mean for batteries to be included in a language?")'From Python document: It is often described as a "batteries included" language due to its comprehensive standard library.Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python 0.9.FromCdocument:Itwasdesignedtobecompiledtoprovidelow-levelaccesstomemoryandlanguageconstructsthatmapefficientlytomachineinstructions,allwithminimalruntimesupport.'Full documentationSpeedThis package currently outperforms Hugging Facetransformersfor CPU inference thanks to int8 quantization and theCTranslate2backend. The following table compares CPU inference performance on identical models using the best available quantization on a 20 question test set.BackendInference TimeMemory UsedHugging Face transformers22s1.77GBThis package11s0.34GBNote that quantization does technically harm output quality slightly, but it should be negligible at this level.ModelsSensible default models are provided. The package should improve over time as stronger models become available. The basic models used are 1000x smaller than the largest models in use today. They are useful as learning tools, but perform far below the current state of the art.Here are the current default models used by the package for a suppliedmax_ramvalue:max_ramModel NameParameters (B)0.5LaMini-Flan-T5-248M0.2481.0LaMini-Flan-T5-783M0.7832.0LaMini-Flan-T5-783M0.7834.0flan-alpaca-gpt4-xl3.08.0openchat-3.5-01067.0For code completions, theCodeT5+series of models are used.Commercial UseThis package itself is licensed for commercial use, but the models used may not be compatible with commercial use. In order to use this package commercially, you can filter models by license type using therequire_model_licensefunction.>>>importlanguagemodelsaslm>>>lm.config['instruct_model']'LaMini-Flan-T5-248M-ct2-int8'>>>lm.require_model_license("apache|bsd|mit")>>>lm.config['instruct_model']'flan-t5-base-ct2-int8'It is recommended to confirm that the models used meet the licensing requirements for your software.Projects IdeasOne of the goals for this package is to be a straightforward tool for learners and educators exploring how large language models intersect with modern software development. It can be used to do the heavy lifting for a number of learning projects:CLI Chatbot (seeexamples/chat.py)Streamlit chatbot (seeexamples/streamlitchat.py)Chatbot with information retrievalChatbot with access to real-time informationTool useText classificationExtractive question answeringSemantic search over documentsDocument question answeringSeveral example programs and notebooks are included in theexamplesdirectory.