Summarization with GGUF Models

answer the question: Anyone know of an embedding model for summarizing documents? I'm the developer of d.ai, a decentralized AI assistant that runs completely offline on mobile. I'm working on improving its ability to process long documents efficiently, and I'm trying to figure out the best way to generate summaries using embeddings. Right now, I use an embedding model for semantic search, but I was wondering—are there any embedding models designed specifically for summarization? Or would I need to take a different approach, like chunking documents and running a transformer-based summarizer on top of the embeddings? Upvote 1 Downvote 3 Go to comments Share Share Comments Section Single comment thread See full discussion u/KonradFreeman avatar KonradFreeman • 56m ago Use a mobile-friendly embedding model like all-MiniLM-L6-v2 from Sentence Transformers to vectorize chunks. This model is small ~22MB and efficient for offline use. Use DistilBART-CNN philschmid/distilbart-cnn-12-6 ~300MB for abstractive summaries. It’s 60% smaller than BART-large but retains 95% of its performance. For extractive summaries, pair embeddings with TextRank or BERT Extractive Summarizer. Upvote 1 Downvote Reply reply Award Share Share u/Timely-Jackfruit8885 avatar Timely-Jackfruit8885 OP • 6m ago Interesting! Are there any summarization models available in GGUF format that work with llama.cpp? How do they function, and what is the maximum context window they support?"

For summarization models in GGUF format that work with `llama.cpp`, you might want to check out: 1. **Mistral 7B (or distilled versions)** – Works well for general text summarization when prompted correctly. 2. **Phi-2 converted to GGUF** – Some users have converted Phi-2 models for lightweight summarization. 3. **TinyLlama** – Optimized for low-resource summarization tasks. ### How They Function: - These models don’t inherently use embeddings for summarization but rely on token-based autoregressive generation. - You’d typically **chunk** the document, feed it into the model, and prompt it with `"Summarize the following text:"`. - Some models support **sliding window attention**, which helps handle longer contexts. ### Maximum Context Window: - **Mistral 7B** supports up to **8K tokens** natively. - **LLaMA 2 13B/7B** (converted to GGUF) has a max context of **4K tokens** but can be finetuned for RoPE scaling. - **Phi-2 (converted)** and **TinyLlama** generally work within **2K-4K tokens**. If you need **long-document summarization**, using **sliding windows** with **retrieval-augmented summarization** (embedding search + chunked generation) could be a good approach. Would you like links to pre-converted GGUF models for testing?

yes

Certainly! Here are some GGUF-format models compatible with `llama.cpp` that you can use for summarization tasks: **1. Mistral 7B Instruct v0.2 GGUF** - **Description**: A 7-billion-parameter model fine-tuned for instruction-based tasks, suitable for summarization. - **Download**: Available on Hugging Face: citeturn0search2 - **Usage**: - Install the `huggingface-hub` Python library: ```bash pip install huggingface-hub ``` - Download the model: ```bash huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GGUF mistral-7b-instruct-v0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` **2. Phi-2 GGUF** - **Description**: A model developed by Microsoft, converted to GGUF format for efficient deployment. - **Download**: Available on Hugging Face: citeturn0search1 - **Usage**: - Install the `huggingface-hub` Python library: ```bash pip install huggingface-hub ``` - Download the model: ```bash huggingface-cli download TheBloke/phi-2-GGUF phi-2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` **3. Mistral 7B v0.1 GGUF** - **Description**: An earlier version of the Mistral 7B model, suitable for various language tasks. - **Download**: Available on Hugging Face: citeturn0search0 - **Usage**: - Install the `huggingface-hub` Python library: ```bash pip install huggingface-hub ``` - Download the model: ```bash huggingface-cli download TheBloke/Mistral-7B-v0.1-GGUF mistral-7b-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` **Considerations for Summarization**: - **Functionality**: These models generate summaries through token-based autoregressive generation. - **Context Window**: Be mindful of the maximum context window each model supports. For longer documents, consider chunking the text and summarizing each part individually. - **Prompting**: Use clear prompts like "Summarize the following text:" to guide the model's output. For more details on using these models with `llama.cpp`, refer to the respective model pages linked above. *Note: Ensure you have the necessary system resources to run these models effectively.*

now synthesize the two responses in a short message

For summarization in GGUF format with `llama.cpp`, try **Mistral 7B Instruct**, **Phi-2**, or **TinyLlama**—all optimized for offline use. Mistral 7B supports **8K tokens**, while others handle **2K-4K**. Use **chunking + prompting** for long documents. You can download these from Hugging Face ([TheBloke's GGUF models](https://huggingface.co/TheBloke)). Let me know if you need setup help!