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. 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 · Developed by Mik Tse · 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:
| Problem | Impact |
|---|---|
| Information overload | 2.5 quintillion bytes generated daily; impossible to process manually |
| Speed pressure | Competitors make decisions in minutes, not months |
| Cost constraints | McKinsey charges $50K+ per engagement; Bloomberg Terminal costs $24K/year |
| Analysis paralysis | Too much data, too little actionable insight |
| Talent shortage | Business intelligence analysts are expensive and scarce |
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:
- Real-time market data (via web research tools)
- Professional analysis (structured like a consulting report)
- Actionable recommendations (specific next steps, not vague advice)
- 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.
| Dimension | luwa-01 | GPT-4o | Claude 3.5 | Gemini Pro | Mistral 7B |
|---|---|---|---|---|---|
| Business specialization | 100% dedicated | General-purpose | General-purpose | General-purpose | General-purpose |
| Response latency | ~50ms | 3-5 seconds | 2-4 seconds | 3-5 seconds | 500ms |
| Cost per 1M tokens | $0.02 | $10.00 | $15.00 | $3.50 | $0.25 |
| Self-hostable | Yes (CPU or GPU) | No | No | No | Yes |
| Open weights | Yes (Apache 2.0) | No | No | No | Yes |
| Requires internet | No (works offline) | Yes | Yes | Yes | No |
| Data privacy | 100% local | Cloud-dependent | Cloud-dependent | Cloud-dependent | Local |
| Customizable | Fully | Limited | Limited | No | Fully |
| Runs on CPU | Yes (4GB RAM) | No | No | No | Marginal |
| Model size | 942 MB | ~30 GB | ~25 GB | ~20 GB | 14 GB |
| Tool-calling for business | Native | API-dependent | API-dependent | API-dependent | None |
| Agent-first design | Yes | No | No | No | No |
| Native research decision | Yes (model decides) | Prompt-dependent | Prompt-dependent | Prompt-dependent | None |
| Mode signals | [RESEARCH]/[CONVERSATIONAL] | None | None | None | None |
| Research synthesis | Yes (structured analysis) | Yes | Yes | Yes | No |
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.
| Parameter | Value |
|---|---|
| Parameters | ~500M |
| Hidden size | 896 |
| Attention heads | 14 (4096 dim, GQA with 2 KV heads) |
| Layers | 24 |
| Vocabulary | 152,064 tokens |
| Max context | 32,768 tokens |
| Weight format | safetensors (942 MB) |
| License | Apache 2.0 |
| Latency | ~1-5s on CPU (500M params, no GPU needed) |
| Cost per query | $0 (self-hosted) |
Capabilities
luwa-01 is not a general-purpose chatbot. It is a specialized agent with the following capabilities:
| Capability | Description |
|---|---|
| Market Analysis | Size, growth, segmentation, competitive landscape |
| Tool-Calling | Outputs structured tool signals for web search, scraping, data retrieval |
| Multi-Tool Chains | Designed to chain multiple research tools (currently web_search only) |
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
| Endpoint | Method | Description |
|---|---|---|
/health |
GET | Health check with model status |
/agent |
POST | Full agent flow with SSE streaming |
/chat |
POST | Simple chat (no web research) |
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.
| # | Dataset | Samples | Purpose |
|---|---|---|---|
| 1 | Identity (Rigid) | ~80 | luwa-01 / Mik Tse / ChatPBC family |
| 2 | Conversational | ~35 | Greetings, Q&A, general business advice |
| 3 | Web Research | ~15 | [RESEARCH] signal + tool-call generation |
| 4 | Mixed (Research + Cite) | ~10 | Research with inline source citations |
| 5 | Market Analysis | ~230 | Market sizing, growth analysis, segmentation |
| 6 | Business Consultancy | ~360 | Strategic advice, recommendations, frameworks |
| 7 | Tool-Calling / Web Research | ~290 | Structured tool-call generation, search queries |
Native Decision Signals (v5)
The v5 model was specifically trained to output decision signals that enable autonomous agent behavior, plus synthesis capability:
| Signal | Meaning | Agent Behavior |
|---|---|---|
[RESEARCH] |
Query requires real-time data | Perform web search, cite sources, then synthesize |
[CONVERSATIONAL] |
Can answer from knowledge | Respond directly, no search |
[TOOL_CALL]{...} |
Specific search query | Execute the specified tool |
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
luwa-01 is part of the ChatPBC ecosystem of specialized business AI models.
Repository: huggingface.co/chatpbc1/luwa-01
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