infactory-ai/infactory_pulse1-4b

Infactory Pulse 1 (4B)

Pulse 1 is a narrative intelligence model for extracting structured storylines from news and editorial content.

What It Does

Given an article, Pulse 1 extracts storylines — not just topics or keywords, but structured narratives that answer:

• WHO is acting (actor)
• WHAT they did (action)
• TO WHOM (target)
• WITH WHAT CONSEQUENCE (outcome)

Each storyline includes a concise name, a why explanation, and full Semantic Role Labeling (SRL) decomposition in machine-readable JSON.

Why Storylines?

A storyline is more than a topic. Topics are static categories ("economy", "politics"). Storylines are dynamic narratives that capture cause-and-effect, developing situations, and active debates.

Example:

• Topic: "Federal Reserve"
• Storyline: "Federal Reserve interest rate increases slow U.S. housing market activity"

The storyline captures the actor (Fed), action (rate increases), target (housing market), and outcome (slowdown) — information you can act on.

Model Details

Property	Value
Base model	Gemma 3 4B (text-only)
Architecture	Gemma 3 (34 transformer layers)
Format	MLX safetensors
Precision	bfloat16
Context window	4,096 tokens
Size	~8.5 GB

Output Format

The model returns a JSON object with a topics array. Each storyline has six fields:

{
  "topics": [
    {
      "name": "Federal Reserve interest rate increases slow U.S. housing market activity",
      "why": "The Fed's tightening cycle is making mortgages unaffordable for first-time buyers.",
      "actor": "Federal Reserve",
      "action": "increases interest rates",
      "target": "U.S. housing market",
      "outcome": "mortgage applications fall to decade lows"
    }
  ]
}

Field	Description
name	A specific narrative phrase (6+ words) with concrete actor, action, and outcome
why	One-sentence summary of why this storyline matters
actor	The primary entity taking action (use full names, not abbreviations)
action	What they did or are doing
target	Who or what is affected
outcome	The consequence or result

Quick Start

With Ollama (recommended on Apple Silicon)

# Import the model (from this directory after downloading weights)
ollama create infactory_pulse1_4b -f Modelfile
ollama run infactory_pulse1_4b

Or use the OpenAI-compatible API:

curl -s http://localhost:11434/v1/chat/completions -d '{
  "model": "infactory_pulse1_4b",
  "messages": [
    {"role": "user", "content": "Identify the key storylines discussed in this article.\n\nArticle:\nTitle: Fed Cuts Rates\nText: The Federal Reserve cut interest rates by 25 basis points today, citing slowing inflation and a cooling labor market."}
  ]
}'

Ollama runs Gemma 3 natively on Apple Silicon (MLX backend) and on CUDA for NVIDIA hardware. The Modelfile sets num_ctx=4096, temperature=0.3, top_p=0.9, and the appropriate Gemma 3 chat template.

With mlx_lm

For direct MLX inference:

pip install mlx-lm
python -m mlx_lm.generate \
  --model ./ \
  --prompt 'Identify the key storylines discussed in this article. ...'

Intended Use

Pulse 1 is designed for narrative intelligence workflows where you need to understand not just what an article is about, but what is happening — the actors, actions, and consequences.

Content Intelligence Pipelines

• Storyline extraction — Convert unstructured articles into structured narrative data
• Salience scoring — Score sentences against extracted storylines to find the most relevant passages
• Entity resolution — Ground storylines in detected entities for richer metadata
• Semantic search — Index and retrieve content by narrative dimensions (actor, action, target, outcome)

Media Monitoring & Analytics

• Narrative tracking — Monitor how storylines evolve across publications over time
• Trend detection — Identify emerging storylines by aggregating across article streams
• Brand-adjacent content discovery — Find articles whose narratives align with brand themes
• Competitive intelligence — Track storylines mentioning specific companies, products, or people

Editorial & Publishing Workflows

• Automated tagging — Generate structured metadata for content management systems
• Pull quote extraction — Score article sentences to surface the most quotable passages
• Evergreen content discovery — Find archival articles newly relevant to today's storylines
• Newsletter curation — Cluster and summarize articles by shared narratives

Research & Analysis

• Narrative framing analysis — Study how different publications frame the same events
• Discourse mapping — Understand the actors and relationships in a topic area
• Information extraction — Build structured datasets from news corpora

Out-of-Scope Use

• General-purpose chat or instruction following
• Languages other than English
• Domains outside news and editorial content
• Real-time or safety-critical applications

Files

File	Description
model-00001-of-00002.safetensors	Model weights (shard 1/2)
model-00002-of-00002.safetensors	Model weights (shard 2/2)
model.safetensors.index.json	Weight shard index
config.json	Model architecture configuration
tokenizer.json	Fast tokenizer
tokenizer_config.json	Tokenizer settings
chat_template.jinja	Chat template (Gemma turn format)
Modelfile	Ollama-compatible model definition

License

This model is a fine-tuned derivative of Google's Gemma 3 and inherits the Gemma Terms of Use.

• Permitted: Research, personal use, and commercial use in products and services.
• Required: You must agree to the Gemma Terms of Use before downloading or using the weights. Redistributions must include the license terms.
• Prohibited: Use for unlawful purposes, generating harmful content, or circumventing safety filters. The Gemma Prohibited Use Policy applies.
• Attribution: Derivative models must acknowledge Gemma as the base model.

The Infactory-specific fine-tuning is proprietary to Infactory AI.

Links

• Infactory
• Gemma Terms of Use