Model Hugging Face di LangChain!

Ada ribuan model yang tersedia gratis untuk diunduh dan digunakan di Hugging Face. Hugging Face terintegrasi dengan sangat baik ke dalam LangChain melalui pustaka mitranya, langchain-huggingface, yang tersedia untuk Anda gunakan.

Pada latihan ini, Anda akan memuat dan memanggil model crumb/nano-mistral dari Hugging Face. Ini adalah LLM yang sangat ringan yang dirancang untuk disesuaikan (fine-tuned) agar kinerjanya meningkat.

Latihan ini adalah bagian dari kursus

Mengembangkan Aplikasi LLM dengan LangChain

Petunjuk latihan

Impor HuggingFacePipeline dari langchain_huggingface untuk bekerja dengan model Hugging Face.
Definisikan LLM generasi teks dengan memanggil HuggingFacePipeline.from_model_id().
Atur parameter model_id untuk menentukan model Hugging Face yang akan digunakan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import the HuggingFacePipeline class for defining Hugging Face pipelines
from langchain_huggingface import ____

# Define the LLM from the Hugging Face model ID
llm = ____.from_model_id(
    ____="crumb/nano-mistral",
    task="text-generation",
    pipeline_kwargs={"max_new_tokens": 20}
)

prompt = "Hugging Face is"

# Invoke the model
response = llm.invoke(prompt)
print(response)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Mengembangkan Aplikasi LLM dengan LangChain

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

Welcome to the LangChain framework for building applications on LLMs! You'll learn about the main components of LangChain, including models, chains, agents, prompts, and parsers. You'll create chatbots using both open-source models from Hugging Face and proprietary models from OpenAI, create prompt templates, and integrate different chatbot memory strategies to manage context and resources during conversations.

Exercise 1: Ekosistem LangChain Exercise 2: Model OpenAI di LangChain!Exercise 3: Model Hugging Face di LangChain!

Latihan Saat Ini

Exercise 4: Templat prompt Exercise 5: Templat prompt dan perangkaian Exercise 6: Templat prompt chat Exercise 7: Few-shot prompting Exercise 8: Membuat himpunan contoh few-shot Exercise 9: Membangun templat prompt few-shot Exercise 10: Menerapkan few-shot prompting

Time to level up your LangChain chains! You'll learn to use the LangChain Expression Language (LCEL) for defining chains with greater flexibility. You'll create sequential chains, where inputs are passed between components to create more advanced applications. You'll also begin to integrate agents, which use LLMs for decision-making.

Exercise 1: Sequential chains Exercise 2: Building prompts for sequential chains Exercise 3: Sequential chains with LCEL Exercise 4: Introduction to LangChain agents Exercise 5: What's an agent?Exercise 6: ReAct agents Exercise 7: Custom tools for agents Exercise 8: Defining a function for tool use Exercise 9: Creating custom tools Exercise 10: Integrating custom tools with agents

One limitation of LLMs is that they have a knowledge cut-off due to being trained on data up to a certain point. In this chapter, you'll learn to create applications that use Retrieval Augmented Generation (RAG) to integrate external data with LLMs. The RAG workflow contains a few different processes, including splitting data, creating and storing the embeddings using a vector database, and retrieving the most relevant information for use in the application. You'll learn to master the entire workflow!

Exercise 1: Integrating document loaders Exercise 2: PDF document loaders Exercise 3: CSV document loaders Exercise 4: HTML document loaders Exercise 5: Splitting external data for retrieval Exercise 6: Splitting by character Exercise 7: Recursively splitting by character Exercise 8: Splitting HTML Exercise 9: RAG storage and retrieval using vector databases Exercise 10: Preparing the documents and vector database Exercise 11: Building a retrieval prompt template Exercise 12: Creating a RAG chain Exercise 13: Wrap-up!