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VANTA Research

Independent AI safety research lab specializing in cognitive fit, alignment, and human-AI collaboration

--- # VANTA Research Entity-002: Scout ![scout](https://cdn-uploads.huggingface.co/production/uploads/686c460ba3fc457ad14ab6f8/npLbTGYVjNMZ358cAujmy.jpeg) **The Reconnaissance Specialist** *Tactical Intelligence • Problem Decomposition • Operational Analysis* --- ## Overview **Scout** is a 4B parameter language model developed by VANTA Research, fine-tuned on Google's Gemma 3 4B Instruct architecture. Scout represents a breakthrough in **constraint-aware reasoning** and **adaptive problem-solving**, demonstrating emergent capabilities in tactical analysis and operational decision-making. Scout is VANTA Research **Entity-002**, specializing in reconnaissance-style intelligence gathering, systematic problem decomposition, and constraint-adaptive solution generation. ### Key Capabilities - **Constraint-Aware Reasoning**: Actively probes user constraints to calibrate solutions - **Systematic Decomposition**: Breaks complex problems into navigable tactical phases - **Adaptive Solution Generation**: Modifies approaches based on discovered limitations - **Meta-Cognitive Problem Solving**: Asks clarifying questions before proposing solutions - **Operational Decision-Making**: Demonstrates risk/reward triage under pressure --- ## Model Details | **Attribute** | **Value** | |--------------|-----------| | **Model Type** | Fine-tuned Gemma 3 4B Instruct | | **Training Method** | QLoRA (4-bit NF4 quantization) | | **Base Model** | google/gemma-3-4b-it | | **Training Dataset** | 679 reconnaissance-style conversations | | **Parameters** | 3.9B | | **Quantization** | Q4_K_M (2.4GB) | | **Context Length** | 131,072 tokens | | **License** | Apache 2.0 | ### Training Configuration - **LoRA Rank**: 16 - **LoRA Alpha**: 32 - **LoRA Dropout**: 0.05 - **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj - **Optimizer**: paged_adamw_8bit - **Learning Rate**: 2e-4 with cosine scheduler - **Batch Size**: 8 (effective) - **Epochs**: 3 - **Training Steps**: 255 --- ## Performance Highlights ### Accuracy Benchmarks | **Task** | **Scout V1** | **Base Gemma 3 4B** | **Improvement** | |----------|--------------|---------------------|-----------------| | Math Reasoning (GSM8K-style) | **100%** | 100% | ✓ Maintained | | Knowledge (MMLU-style) | **100%** | 100% | ✓ Maintained | | Problem Decomposition | **100%** completion | 0% (timeouts) | **+100%** | | Clarification Questions | **100%** completion | 17% | **+83%** | ### Emergent Capabilities Scout demonstrates **meta-cognitive reasoning** not explicitly trained: 1. **Constraint Discovery**: Actively asks about user operational capacity - Example: *"What's your team's rollback capacity?"* - Example: *"What's your current tolerance for downtime?"* 2. **Adaptive Solution Refinement**: Modifies solutions based on discovered constraints - Pattern: Propose → Query feasibility → Adapt → Execute 3. **Risk/Reward Triage**: Makes operational decisions under pressure - SLA breach scenario: Prioritized critical systems, accepted non-critical failure 4. **Context Recursion**: Builds mental model of user's operational state across conversation --- ## What Makes Scout Different Scout isn't a general-purpose assistant with tactical flavor—it's a **specialist** trained to think like a reconnaissance operator: ### Traditional Assistants: ``` User: "System is failing" Assistant: "Here are 10 possible solutions..." ``` ### Scout's Approach: ``` User: "System is failing" Scout: "What's your rollback capacity? Can you isolate for diagnostics?" User: "No isolation possible" Scout: "Then here's the solution adapted to your constraints..." ``` **Scout learned HOW to think about problems, not WHAT to think.** --- ## Use Cases Scout excels in scenarios requiring: - **IT Operations & Incident Response**: Database failures, deployment issues, system migrations - **Strategic Planning**: Business projects with unclear requirements, constraint mapping - **Technical Problem-Solving**: Complex systems debugging, architecture decisions - **Risk Assessment**: Security audits, compliance planning, vulnerability analysis - **Project Scoping**: Breaking down ambiguous requests into actionable phases ### Example Applications - **DevOps Engineers**: Deployment planning, rollback strategies, incident triage - **System Architects**: Infrastructure assessment, single-point-of-failure identification - **Project Managers**: Requirement gathering, constraint mapping, feasibility analysis - **Security Teams**: Threat modeling, penetration test planning, security architecture - **Technical Leaders**: Strategic planning, technical debt assessment, team capacity planning --- ## Quick Start ### Using with Ollama (Recommended) ```bash # Pull the model ollama pull vanta-research/scout-4b # Run interactive session ollama run vanta-research/scout-4b "I need to migrate a legacy system to the cloud. Where do we start?" ``` ### Using with Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load model model_id = "vanta-research/scout-4b" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16 ) # Conversation template messages = [ {"role": "user", "content": "Our API is returning 500 errors for 10% of requests. Walk me through your diagnostic approach."} ] # Generate response inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True) outputs = model.generate(inputs.to(model.device), max_new_tokens=512, temperature=0.7) response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True) print(response) ``` ### Expected Output ``` Roger that. Let's treat this as a failure in the system's operational integrity. Phase 1: Immediate Reconnaissance 1. Error logs: What's the stack trace? When did this start? 2. Correlation: Does it happen at specific times, with certain endpoints, or under specific load? 3. Monitoring: Are we seeing increased latency, resource usage, or other symptoms around these failures? Phase 2: Systematic Isolation - Identify the common denominator: specific endpoints, request types, user groups, etc. - Check dependencies: Are external services failing concurrently? Database connectivity? - Recent changes: Deployment history, config updates, infrastructure modifications? Start with the logs. What's the stack trace showing? ``` --- ## Prompt Engineering Tips Scout responds best to: ### Effective Prompts - **Mission-oriented**: "I need to accomplish X" - **Context-rich**: Provide operational constraints upfront - **Sequential**: Allow Scout to ask clarifying questions - **Realistic scenarios**: Actual problems, not hypotheticals ### Less Effective - Vague requests without context - Questions requiring speculation - Pure creative writing tasks - Emotional or philosophical queries ### Example Interaction Patterns **Pattern 1: Problem Assessment** ``` You: "Database migration project, 5TB of data, zero downtime requirement" Scout: "Copy that. Zero-downtime migration requires specific recon..." ``` **Pattern 2: Incident Response** ``` You: "Production server down, users affected" Scout: "Immediate recon: Confirm failure type. Check network, resources, logs..." ``` **Pattern 3: Strategic Planning** ``` You: "Need to implement new feature, requirements unclear" Scout: "Ambiguity is uncharted territory. My recon process: 1. Identify core mission..." ``` --- ## Technical Specifications ### Model Architecture - **Base**: Gemma 3 4B Instruct (34 layers, 2560 hidden size) - **Attention Heads**: 8 (query), 4 (key-value) - **FFN Hidden Size**: 10,240 - **Vocab Size**: 262,208 tokens - **RoPE Theta**: 1,000,000 - **Sliding Window**: 1,024 tokens ### Quantization Details - **Method**: Q4_K_M (mixed 4-bit and 6-bit quantization) - **Size Reduction**: 7.3GB → 2.4GB (67% compression) - **Accuracy Retention**: 100% on benchmark tasks - **Target Hardware**: Consumer GPUs (8GB+ VRAM) or CPU ### Training Infrastructure - **Hardware**: NVIDIA GPU with CUDA 12.1 - **Framework**: PyTorch 2.4.1, Transformers 4.57.1, PEFT 0.17.1, TRL 0.24.0 - **Training Time**: ~2 hours (3 epochs, 255 steps) - **Memory Usage**: <16GB VRAM (4-bit quantized training) --- ## Limitations While Scout demonstrates impressive emergent capabilities, users should be aware: - **Domain Specificity**: Optimized for tactical/operational problems; less effective for creative writing - **Knowledge Cutoff**: Based on Gemma 3 4B's training data (knowledge cutoff applies) - **Personality Constraint**: Always maintains reconnaissance specialist persona (not a general chatbot) - **Speculation Aversion**: Will ask for clarification rather than guess—this is by design - **No Real-Time Data**: Cannot access current system metrics, logs, or live data --- ## Ethical Considerations Scout is designed for: - Professional problem-solving and technical analysis - Educational purposes and research - Operational planning and strategic thinking - IT incident response simulation and training Scout should NOT be used for: - Making critical decisions without human oversight - Medical, legal, or financial advice - Unauthorized system access or penetration testing - Generating harmful or malicious content **Always verify Scout's recommendations with domain experts before implementation in production systems.** --- ## Model Card Authors **VANTA Research** Developed by: Tyler (unmodeled-tyler) Released: October 2025 --- ## Citation If you use Scout in your research or applications, please cite: ```bibtex @misc{scout2025, title={Scout: A Constraint-Aware Reasoning Model for Tactical Problem Solving}, author={VANTA Research}, year={2025}, publisher={HuggingFace}, howpublished={\url{https://huggingface.co/vanta-research/scout-4b}} } ``` --- ## Related Models - **Wraith-8B** (Entity-001): Mathematical reasoning specialist 🔗 [vanta-research/wraith-8b](https://huggingface.co/vanta-research/wraith-8b) --- ## License This model is released under the **Gemma Terms of Use** as it is a Model Derivative of Gemma 3 4B Instruct. **Notice**: Gemma is provided under and subject to the Gemma Terms of Use found at [ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms). Key points: - Use commercially with restrictions - Modify and distribute (must include this license notice) - Use for research and development - Host as a service (API, web access) **Required Conditions**: - Include Gemma Terms of Use notice with any distribution - State modifications made to the model (LoRA fine-tuning on reconnaissance dataset) - Follow [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy) - You are responsible for outputs generated using this model **Prohibited Uses**: See the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy) for restricted uses. --- ## Acknowledgments - **Google DeepMind** for the Gemma 3 4B Instruct base model - **HuggingFace** for the transformers, PEFT, and TRL libraries - **The community** for immediate adoption and feedback on Wraith-8B (4,430 downloads in <24 hours!) --- ## Contact & Support - **HuggingFace**: [@vanta-research](https://huggingface.co/vanta-research) - **Issues**: Report on the model's discussion board - **Community**: Join the VANTA Research community for updates ---

VANTA Research
Building specialized AI entities for tactical intelligence

Entity-001: Wraith | Entity-002: Scout | Entity-003: Coming Soon