Instructions to use QuantFactory/HTML-Pruner-Llama-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/HTML-Pruner-Llama-1B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/HTML-Pruner-Llama-1B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/HTML-Pruner-Llama-1B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/HTML-Pruner-Llama-1B-GGUF",
	filename="HTML-Pruner-Llama-1B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/HTML-Pruner-Llama-1B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/HTML-Pruner-Llama-1B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/HTML-Pruner-Llama-1B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/HTML-Pruner-Llama-1B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/HTML-Pruner-Llama-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/HTML-Pruner-Llama-1B-GGUF to start chatting

Pi new

How to use QuantFactory/HTML-Pruner-Llama-1B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/HTML-Pruner-Llama-1B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/HTML-Pruner-Llama-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/HTML-Pruner-Llama-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/HTML-Pruner-Llama-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.HTML-Pruner-Llama-1B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/HTML-Pruner-Llama-1B-GGUF

This is quantized version of zstanjj/HTML-Pruner-Llama-1B created using llama.cpp

Original Model Card

✨ Latest News

[11/06/2024]: Our paper is available on arXiv. You can access it here.
[11/05/2024]: The open-source toolkit and models are released. You can apply HtmlRAG in your own RAG systems now.

Model Information

• 📝 Paper • 🤗 Hugging Face • 🧩 Github

We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.

Lossless HTML Cleaning: This cleaning process just removes totally irrelevant contents and compress redundant structures, retaining all semantic information in the original HTML. The compressed HTML of lossless HTML cleaning is suitable for RAG systems that have long-context LLMs and are not willing to loss any information before generation.
Two-Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps, both of which are conducted on the block tree structure. The first pruning step uses a embedding model to calculate scores for blocks, while the second step uses a path generative model. The first step processes the result of lossless HTML cleaning, while the second step processes the result of the first pruning step.

🌹 If you use this model, please star our GitHub repository to support us. Your star means a lot!

📦 Installation

Install the package using pip:

pip install htmlrag

Or install the package from source:

pip install -e .

📖 User Guide

🧹 HTML Cleaning

from htmlrag import clean_html

question = "When was the bellagio in las vegas built?"
html = """
<html>
<head>
<title>When was the bellagio in las vegas built?</title>
</head>
<body>
<p class="class0">The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>
</body>
<div>
<div>
<p>Some other text</p>
<p>Some other text</p>
</div>
</div>
<p class="class1"></p>
<!-- Some comment -->
<script type="text/javascript">
document.write("Hello World!");
</script>
</html>
"""

simplified_html = clean_html(html)
print(simplified_html)

# <html>
# <title>When was the bellagio in las vegas built?</title>
# <p>The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>
# <div>
# <p>Some other text</p>
# <p>Some other text</p>
# </div>
# </html>

🌲 Build Block Tree

from htmlrag import build_block_tree

block_tree, simplified_html = build_block_tree(simplified_html, max_node_words=10)
for block in block_tree:
    print("Block Content: ", block[0])
    print("Block Path: ", block[1])
    print("Is Leaf: ", block[2])
    print("")

# Block Content:  <title>When was the bellagio in las vegas built?</title>
# Block Path:  ['html', 'title']
# Is Leaf:  True
# 
# Block Content:  <div>
# <p>Some other text</p>
# <p>Some other text</p>
# </div>
# Block Path:  ['html', 'div']
# Is Leaf:  True
# 
# Block Content:  <p>The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>
# Block Path:  ['html', 'p']
# Is Leaf:  True

✂️ Prune HTML Blocks with Embedding Model

from htmlrag import EmbedHTMLPruner

embed_html_pruner = EmbedHTMLPruner(embed_model="bm25")
block_rankings = embed_html_pruner.calculate_block_rankings(question, simplified_html, block_tree)
print(block_rankings)

# [0, 2, 1]

from transformers import AutoTokenizer

chat_tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-70B-Instruct")

max_context_window = 60
pruned_html = embed_html_pruner.prune_HTML(simplified_html, block_tree, block_rankings, chat_tokenizer, max_context_window)
print(pruned_html)

# <html>
# <title>When was the bellagio in las vegas built?</title>
# <p>The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>
# </html>

✂️ Prune HTML Blocks with Generative Model

from htmlrag import GenHTMLPruner

ckpt_path = "zstanjj/HTML-Pruner-Llama-1B"
gen_embed_pruner = GenHTMLPruner(gen_model=ckpt_path, max_node_words=10)
block_rankings = gen_embed_pruner.calculate_block_rankings(question, pruned_html)
print(block_rankings)

# [1, 0]

max_context_window = 32
pruned_html = gen_embed_pruner.prune_HTML(pruned_html, block_tree, block_rankings, chat_tokenizer, max_context_window)
print(pruned_html)

# <p>The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada. It was built in 1998.</p>

Results

Results for HTML-Pruner-Phi-3.8B and HTML-Pruner-Llama-1B with Llama-3.1-70B-Instruct as chat model.

Dataset	ASQA	HotpotQA	NQ	TriviaQA	MuSiQue	ELI5
Metrics	EM	EM	EM	EM	EM	ROUGE-L
BM25	49.50	38.25	47.00	88.00	9.50	16.15
BGE	68.00	41.75	59.50	93.00	12.50	16.20
E5-Mistral	63.00	36.75	59.50	90.75	11.00	16.17
LongLLMLingua	62.50	45.00	56.75	92.50	10.25	15.84
JinaAI Reader	55.25	34.25	48.25	90.00	9.25	16.06
HtmlRAG-Phi-3.8B	68.50	46.25	60.50	93.50	13.25	16.33
HtmlRAG-Llama-1B	66.50	45.00	60.75	93.00	10.00	16.25

📜 Citation

@misc{tan2024htmlraghtmlbetterplain,
      title={HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems}, 
      author={Jiejun Tan and Zhicheng Dou and Wen Wang and Mang Wang and Weipeng Chen and Ji-Rong Wen},
      year={2024},
      eprint={2411.02959},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2411.02959}, 
}

Downloads last month: 242

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for QuantFactory/HTML-Pruner-Llama-1B-GGUF

Base model

meta-llama/Llama-3.2-1B

Quantized

(244)

this model

Paper for QuantFactory/HTML-Pruner-Llama-1B-GGUF

HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems

Paper • 2411.02959 • Published Nov 5, 2024 • 71