luwa-01

Experimental Open Source retrieval-assisted business research AI.

luwa-01 is experimental, open source retrieval-assisted business research AI model built by ChatPBC Labs. It is designed for one purpose: delivering expert-level market analysis, business strategy, and competitive intelligence — powered by real-time web research through an integrated agent framework.

Version: v5 · Built by ChatPBC Labs · Documentation · Live Playground · Hugging Face

Why luwa-01 Exists

In 2026, businesses face an unprecedented information crisis. The speed of market change has accelerated to the point where traditional research — hiring consultants, commissioning reports, waiting weeks for analysis — is simply too slow. By the time a traditional report is delivered, the opportunity has often passed.

The critical problems businesses face today:

luwa-01 solves all five problems simultaneously. It provides business-focused analysis, near-instant responses on CPU, and zero API costs — all with real-time web access through its agent tool-calling framework.

How Businesses Use luwa-01

luwa-01 is deployed across multiple business functions:

Market Entry Analysis — Before entering a new market, businesses query luwa-01 for size, growth, competition, and barriers to entry. It synthesizes real-time data into a decision-ready report in seconds.

Competitive Intelligence — luwa-01 monitors competitors, analyzes their positioning, pricing, and product launches, and delivers strategic recommendations on how to differentiate.

Trend Forecasting — For strategic planning, luwa-01 forecasts industry trends 1-5 years out, identifying emerging opportunities before competitors do.

Business Validation — Entrepreneurs and corporate innovation teams use luwa-01 to validate business ideas, assess market fit, and score opportunities against key criteria.

Investment Research — Analysts use luwa-01 to quickly assess sectors, identify investment opportunities, and generate preliminary due diligence.

The Critical Problem luwa-01 Solves in 2026

The main problem in 2026 is not a lack of information — it's the inability to act on information fast enough. The window between "opportunity available" and "opportunity lost" has shrunk from months to days, sometimes hours.

luwa-01 compresses this decision loop to under a second (on CPU). A business that queries luwa-01 gets:

1. Real-time market data (via web research tools)
2. Professional analysis (structured like a consulting report)
3. Actionable recommendations (specific next steps, not vague advice)
4. Risk assessment (what could go wrong and how to mitigate it)

This is the difference between reacting to the market and shaping it.

luwa-01 vs General-Purpose Models

luwa-01 is not designed to replace GPT-4o or Claude. It is designed to outperform them at one specific job: business intelligence.

Why Businesses Should Choose luwa-01

1. Speed. Real-time decisions require real-time answers. luwa-01 responds in under a second on CPU — fast enough for interactive use. No GPU required.

2. Cost. luwa-01 runs on free CPU hardware. No API costs, no per-token billing. A business can run unlimited queries for $0 — just electricity.

3. Privacy — your competitive intelligence never leaves your infrastructure. When you're researching a market entry strategy, that data is proprietary. luwa-01 runs entirely on your hardware.

4. Control — full ownership. Apache 2.0 license means you can modify, fine-tune, redistribute, and build commercial products on top without restrictions.

5. Specialization. luwa-01 is trained specifically for business intelligence tasks. Its parameters are dedicated to market analysis, strategy, and business consultancy — not general-purpose chat.

6. Agent-first design. luwa-01 was built from the ground up as an agent, not a chatbot. Its tool-calling interface is native, not bolted on.

7. Native decision-making. The model itself decides when to research the web vs. when to answer directly from its knowledge. It outputs [RESEARCH] or [CONVERSATIONAL] signals that the agent framework interprets — no external routing needed.

Model Architecture

luwa-01 is a transformer-based language model optimized for business intelligence tasks.

Capabilities

luwa-01 is not a general-purpose chatbot. It is a specialized agent with the following capabilities:

Agent Framework

luwa-01 has native agent decision-making built into the model. The model itself outputs decision signals that determine its behavior:

• [RESEARCH] — The model recognizes the query requires current web data. The agent performs real-time web search with citations.
• [CONVERSATIONAL] — The model answers directly from its training knowledge (identity, greetings, general advice).
• [TOOL_CALL] — When in research mode, the model specifies its search query:

[TOOL_CALL]{"tool": "web_search", "parameters": {"query": "AI market size 2026"}}[/TOOL_CALL]

This is a NATIVE model capability — the model was trained to make this decision autonomously. No external routing or classification is needed.

Available tools:

• web_search — Real-time web search via DuckDuckGo HTML scraping (currently implemented)

Additional tools (web_scrape, market_data, news_analysis, competitor_analysis, trend_forecast) are designed but not yet implemented. The model generates tool-call signals for these, but only web_search is currently wired up in the agent runtime.

The agent loop: Model Decides → Parse Signal → Execute → Analyze → Synthesize → Deliver

The agent is also available as a live demo — see the Try luwa-01 page in the documentation. It runs on a real-time FastAPI server with SSE streaming, showing live activity (researching, found, reading, thinking) and source citations as clickable URLs.

Quick Start

Python (with transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "chatpbc1/luwa-01",
    trust_remote_code=True,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("chatpbc1/luwa-01")

prompt = "Analyze the AI market in 2026: size, growth, key players, and opportunities"
messages = [{"role": "user", "content": prompt}]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Via API (Agent Runtime)

curl -X POST "https://your-url/agent" \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Analyze the e-commerce market in Southeast Asia", "session_id": "abc123", "max_tokens": 512}'

The agent returns SSE events for each activity step, followed by the final response with citations:

{"type": "thinking", "message": "luwa-01 is thinking..."}
{"type": "researching", "message": "Researching the web..."}
{"type": "found", "message": "Found 5 relevant sources"}
{"type": "reading", "message": "Analyzing sources..."}
{"type": "thinking", "message": "luwa-01 is analyzing..."}
{"response": "...", "citations": [{"index": 1, "title": "...", "url": "..."}], "mode": "research"}

The mode field in the final response indicates the model's decision:

• "mode": "research" — Model chose to research the web
• "mode": "conversational" — Model answered directly from knowledge

Agent REST API Endpoints

Production Readiness Checklist

• Model format: safetensors (safe, fast loading)
• Tokenizer: Full 152K vocab tokenizer with chat template
• Generation config: Optimized temperature (0.7), top_p (0.9), repetition penalty (1.1), no_repeat_ngram (3)
• System prompt: Business intelligence specialist persona
• Agent config: 6 tools, structured agent loop, confidence thresholding
• Inference flag: inference: true — ready for HF Inference API
• License: Apache 2.0 — commercial use permitted
• Size: 942 MB — fits on any GPU, even CPU (4GB RAM)
• No external dependencies: All files self-contained in the repo

File Structure

luwa-01/
├── model.safetensors        # Model weights (942 MB)
├── config.json              # Architecture configuration
├── generation_config.json   # Optimized generation parameters
├── tokenizer.json           # Fast tokenizer (152K vocab)
├── tokenizer_config.json    # Tokenizer settings
├── chat_template.jinja      # Chat formatting template
├── README.md                # This documentation
├── system_prompt.txt        # Business intelligence system prompt
├── agent_config.json        # Agent tools and configuration

Limitations

• Knowledge cutoff: Training data reflects knowledge up to the base model's cutoff
• Web access requires agent wrapper: The model itself doesn't browse the web — it signals the agent to do so
• 500M size: Not as capable as 70B+ models on complex multi-hop reasoning, but dramatically faster and cheaper
• English primary: Optimized for English business content
• Synthesis depth: The model's synthesis is good but not perfect — it may occasionally oversimplify or repeat patterns for very complex multi-domain queries

Training Data

The model was developed using 7 specialized synthetic datasets totaling ~1,000 training samples across 5 training iterations (v1-v5). The current model is v5.

Native Decision Signals (v5)

The v5 model was specifically trained to output decision signals that enable autonomous agent behavior, plus synthesis capability:

v5 Improvements

• Synthesis: After web research, the model synthesizes the data into structured analysis with key findings, recommendations, and inline citations
• Rigid mode distinction: The model decisively chooses between research and conversational modes
• EOS token removal: Responses no longer contain the model's internal special EOS marker (model's raw output includes an extra token that is stripped by the runtime)
• Diverse responses: The model generates varied, context-aware responses instead of repeating the same pattern

License

Apache License 2.0 — Free for commercial use, modification, and distribution.

Built by ChatPBC Labs

luwa-01 is part of the ChatPBC ecosystem of specialized business AI models.

Repository: huggingface.co/chatpbc1/luwa-01