Entity-002: Scout 4B 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

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

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)

# 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

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
Released: October 2025

Citation

If you use Scout in your research or applications, please cite:

@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
• Find Scout’s weights on Hugging Face

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.

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 - You are responsible for outputs generated using this model

Prohibited Uses: See the 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

• Email: hello@vantaresearch.xyz
• Website: vantaresearch.xyz