---
license: gemma
language:
- en
tags:
- reasoning
- tactical-analysis
- problem-solving
- reconnaissance
- gemma
- vanta-research
- chat
- conversational-ai
- text
- text-generation
- persona
- personality
- tactical
- general
- LLM
- language-model
- chat
- conversational-ai
base_model: google/gemma-3-4b-it
model_type: gemma3
pipeline_tag: text-generation
library_name: transformers
---

# 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

---

<div align="center">
  <strong>VANTA Research</strong><br/>
  <em>Building specialized AI entities for tactical intelligence</em><br/><br/>
  Entity-001: Wraith | <strong>Entity-002: Scout</strong> | Entity-003: Coming Soon
</div>