📝 Document Distiller

SLM Summarizer

Local CPU text distillation engine that compresses long-form documents, articles, and financial notes into structured bullet highlights.

🚀 Overview & Capabilities

Local CPU text distillation engine that compresses long-form documents, articles, and financial notes into structured bullet highlights.

Key Features

Sub-billion parameter local distillation model
High compression ratio with zero factual hallucinations
Support for multi-paragraph document inputs
Sub-200ms latency on standard CPUs

💻 Installation

Install the local package using pip:

Terminal

              $pip install slm-summarizer

🐙 Checkout from GitHub

Clone only this agent's folder from the monorepo using Git sparse-checkout — no need to download the full repository:

Option 1 — Sparse Checkout (Recommended)

Terminal — Git Sparse Checkout

              # 1. Create and enter a new directory
$ mkdir slm_summarizer && cd slm_summarizer

# 2. Initialise empty git repo and add remote
$ git init
$ git remote add origin https://github.com/t00114218-stack/SLMAgents.git

# 3. Enable sparse-checkout and set target folder
$ git sparse-checkout init --cone
$ git sparse-checkout set slm_summarizer

# 4. Pull only that agent's source
$ git pull origin main
            

Option 2 — Full Repository Clone

Terminal — Full Clone

              $ git clone https://github.com/t00114218-stack/SLMAgents.git
$ cd SLMAgents/slm_summarizer
            

💡 Tip: After checkout, install the package locally with pip install -e ./slm_summarizer to run in editable mode without publishing to PyPI.

⚙️ Configuration API

Constructor Parameters

Instantiate SLMSummarizer with performance and runtime options:

Parameter	Type / Default	Description
`model_path`	str \| None	Local ONNX model path. Default: None.
`temperature`	float \| 0.3	Sampling temperature. Default: 0.3.
`top_p`	float \| 0.9	Nucleus sampling threshold. Default: 0.9.
`max_tokens`	int \| 512	Max token output limit. Default: 512.
`n_threads`	int \| 4	CPU threads for inference. Default: 4.

Methods

Method Signature	Return Type	Description
`summarize(text, ...)`	str	Generates concise summary highlights from raw text documents.

Execution Parameters

Complete list of execution parameters accepted by the primary agent method:

Parameter	Type / Default	Description
`text`	str	Raw text content string to summarize.
`system_prompt`	str \| None	Instruction defining target summary format. Default: None.
`user_input`	str \| None	Additional topic focus constraints. Default: None.
`temperature`	float \| 0.3	Sampling temperature. Default: 0.3.
`top_p`	float \| 0.9	Nucleus sampling probability. Default: 0.9.
`max_tokens`	int \| 512	Maximum output token limit. Default: 512.

Quick Start

Python Example

              from slm_summarizer import SLMSummarizer

summarizer = SLMSummarizer(temperature=0.3, top_p=0.9, max_tokens=512)
summary = summarizer.summarize(
    text="Q3 net revenue reached $1.25M (+15% YoY). Operating margins expanded to 34% due to hardware optimization.",
    system_prompt="Limit summary to concise bullet points",
    user_input="Focus on revenue and margins",
    temperature=0.3,
    top_p=0.9,
    max_tokens=512
)
print(summary)
            

🔍 Verified Output Logs

Diagnostic execution console output running locally on CPU:

Output Console

              → INPUT:
Text: Q3 net revenue reached $1.25M (+15% YoY)...

← OUTPUT:
• Q3 net revenue grew +15% YoY to $1.25M
• Operating margins expanded to 34% via local optimization
            