mirror of
https://github.com/hwchase17/langchain.git
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480626dc99
…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
103 lines
3.4 KiB
Plaintext
103 lines
3.4 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Titan Takeoff Pro\n",
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"\n",
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"`TitanML` helps businesses build and deploy better, smaller, cheaper, and faster NLP models through our training, compression, and inference optimization platform.\n",
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"\n",
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">Note: These docs are for the Pro version of Titan Takeoff. For the community version, see the page for Titan Takeoff.\n",
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"\n",
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"Our inference server, [Titan Takeoff (Pro Version)](https://docs.titanml.co/docs/titan-takeoff/pro-features/feature-comparison) enables deployment of LLMs locally on your hardware in a single command. Most generative model architectures are supported, such as Falcon, Llama 2, GPT2, T5 and many more."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example usage\n",
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"Here are some helpful examples to get started using the Pro version of Titan Takeoff Server.\n",
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"No parameters are needed by default, but a baseURL that points to your desired URL where Takeoff is running can be specified and generation parameters can be supplied."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.callbacks.manager import CallbackManager\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
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"from langchain.prompts import PromptTemplate\n",
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"from langchain_community.llms import TitanTakeoffPro\n",
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"\n",
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"# Example 1: Basic use\n",
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"llm = TitanTakeoffPro()\n",
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"output = llm(\"What is the weather in London in August?\")\n",
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"print(output)\n",
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"\n",
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"\n",
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"# Example 2: Specifying a port and other generation parameters\n",
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"llm = TitanTakeoffPro(\n",
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" base_url=\"http://localhost:3000\",\n",
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" min_new_tokens=128,\n",
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" max_new_tokens=512,\n",
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" no_repeat_ngram_size=2,\n",
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" sampling_topk=1,\n",
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" sampling_topp=1.0,\n",
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" sampling_temperature=1.0,\n",
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" repetition_penalty=1.0,\n",
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" regex_string=\"\",\n",
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")\n",
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"output = llm(\"What is the largest rainforest in the world?\")\n",
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"print(output)\n",
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"\n",
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"\n",
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"# Example 3: Using generate for multiple inputs\n",
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"llm = TitanTakeoffPro()\n",
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"rich_output = llm.generate([\"What is Deep Learning?\", \"What is Machine Learning?\"])\n",
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"print(rich_output.generations)\n",
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"\n",
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"\n",
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"# Example 4: Streaming output\n",
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"llm = TitanTakeoffPro(\n",
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" streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])\n",
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")\n",
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"prompt = \"What is the capital of France?\"\n",
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"llm(prompt)\n",
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"\n",
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"# Example 5: Using LCEL\n",
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"llm = TitanTakeoffPro()\n",
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"prompt = PromptTemplate.from_template(\"Tell me about {topic}\")\n",
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"chain = prompt | llm\n",
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"chain.invoke({\"topic\": \"the universe\"})"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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