llm-search
hyde
| llm-search | hyde | |
|---|---|---|
| 2 | 2 | |
| 398 | 362 | |
| - | 0.0% | |
| 8.2 | 10.0 | |
| 20 days ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | - |
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llm-search
- Querying local documents, powered by LLM
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I can't find a code example of using a prompt in langchain with a GGML quantized llama-based model!
Here is an example how to create a custom class for langchain - https://github.com/snexus/llm-search/blob/main/src/llmsearch/models/llama.py It is calling llama-cpp-python behind the scenes
hyde
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Show HN: Hacker Search – A semantic search engine for Hacker News
HyDE apparently means “Hypothetical Document Embeddings”, which seems to be a kind of generative query expansion/pre-processing
https://arxiv.org/abs/2212.10496
https://github.com/texttron/hyde
From the abstract:
Given a query, HyDE first zero-shot instructs an instruction-following language model (e.g. InstructGPT) to generate a hypothetical document. The document captures relevance patterns but is unreal and may contain false details. Then, an unsupervised contrastively learned encoder~(e.g. Contriever) encodes the document into an embedding vector. This vector identifies a neighborhood in the corpus embedding space, where similar real documents are retrieved based on vector similarity. This second step ground the generated document to the actual corpus, with the encoder's dense bottleneck filtering out the incorrect details.
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Meet HyDE: An Effective Fully Zero-Shot Dense Retrieval Systems That Require No Relevance Supervision, Works Out-of-Box, And Generalize Across Tasks
Quick Read: https://www.marktechpost.com/2023/01/23/meet-hyde-an-effective-fully-zero-shot-dense-retrieval-systems-that-require-no-relevance-supervision-works-out-of-box-and-generalize-across-tasks/ Paper: https://arxiv.org/pdf/2212.10496.pdf Github: https://github.com/texttron/hyde
What are some alternatives?
alpaca_eval - An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.
FastLoRAChat - Instruct-tune LLaMA on consumer hardware with shareGPT data
DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
ReAct - [ICLR 2023] ReAct: Synergizing Reasoning and Acting in Language Models
EasyEdit - An Easy-to-use Knowledge Editing Framework for LLMs.
jupyter-notebook-chatcompletion - Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks!
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
anything-llm - The all-in-one Desktop & Docker AI application with full RAG and AI Agent capabilities.