polaris-oss-3b-base

Polaris-OSS-3B-Base

Model ID: purposebyoriento/polaris-oss-3b-base

A 3B parameter open-source instruction-tuned language model designed for structured explanation generation within deterministic systems. Optimized for transparency, auditability, and controlled output in institutional B2B contexts.

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Model Description

Polaris-OSS-3B-Base is a specialized foundation model that transforms structured data signals into human-readable explanations. It is designed to operate as a controlled transparency layer, not as an autonomous decision-making agent.

Key Characteristics

• Size: ~3 billion parameters
• Task: Structured explanation generation from JSON inputs
• Output Style: Deterministic, factual, neutral tone
• Context Window: Dependent on base architecture (typically 2K-8K tokens)
• Deployment: Optimized for 4-bit quantization

Architecture Role

Rule-Based Matching Engine
         ↓
  Confidence Layer
         ↓
Polaris-OSS-3B (Explanation Adapter)
         ↓
  Validation Layer
         ↓
    User Interface

Polaris does not perform:

• Ranking or filtering decisions
• Eligibility determinations
• Autonomous personalization
• User profiling or diagnostics

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Intended Uses

Primary Use Case: Explanation Generation

Input:

• Structured user attributes (interests, availability, preferences)
• Opportunity metadata (type, location, cost, timing)
• Pre-computed matching signals from rule-based engine

Output:

• 2-4 sentence explanations of why an opportunity was matched
• Attribute-aligned reasoning
• No hallucinated claims or invented attributes

Example:

Input:
{
  "user_attributes": {
    "interests": ["STEM", "leadership"],
    "availability": "summer",
    "cost_preference": "low"
  },
  "opportunity": {
    "title": "Youth Robotics Camp",
    "type": "STEM workshop",
    "season": "summer",
    "cost": "free"
  },
  "match_signals": ["interest_alignment", "time_fit", "cost_fit"]
}

Output:
"This opportunity aligns with your interest in STEM and leadership. It takes 
place during your available time period and fits your cost preference."

Secondary Use Cases (Future Extensions)

• Opportunity content summarization
• Controlled reflection prompt generation
• Content normalization during data ingestion
• Light semantic interpretation of structured fields

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Out-of-Scope Use

❌ Do not use for:

• Autonomous decision-making or ranking
• Diagnostic labeling of users
• Open-ended creative writing
• Medical, legal, or financial advice
• Any use case requiring multi-step reasoning without validation
• Unsupervised personalization

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Training Details

Fine-Tuning Strategy

Polaris-OSS-3B-Base serves as a frozen backbone with task-specific LoRA adapters:

1. Explanation Adapter

ID: polaris-oss-3b-explain-lora-v1

Task: Transform structured matching signals into human-readable explanations

Training Data Schema:

• Input: JSON with user attributes + opportunity metadata
• Output: 2-4 sentence factual explanation
• Format: Instruction-following pairs

2. STARR Adapter (Optional)

ID: starr-oss-3b-clarify-lora-v1

Task: Generate single clarification questions from ambiguous user inputs

Training Data Schema:

• Input: Incomplete or ambiguous user response
• Output: One specific clarifying question
• Constraint: No multi-question chaining

Guardrails

All adapters enforce:

1. Strict Prompt Templates

   • "Use only provided data"
   • "Do not invent attributes"
   • "Do not label the user"

2. Output Validation

   • Regex-based attribute verification
   • Length constraints (50-200 tokens)
   • Fact alignment checks against input

3. Determinism Controls

   • Low temperature (0.3-0.5)
   • Limited max tokens (100)
   • Constrained decoding strategies

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Technical Specifications

Deployment Recommendations

• Production: 4-bit GPTQ/AWQ quantization
• Development: FP16 for fine-tuning
• Batch Size: 1-8 for real-time inference
• Context Length: Keep under 512 tokens for optimal latency

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Evaluation

Performance Characteristics

Strengths ✅

• Fast inference (<100ms)
• Low infrastructure cost
• Sufficient for structured transformation tasks
• Scalable for early-stage B2B deployment
• Deterministic output with proper guardrails

Limitations ⚠️

• Weaker multi-factor reasoning compared to 7B models
• Higher hallucination probability without strict validation
• Less nuanced language generation
• Limited long-term context handling

Benchmarks

Note: Standard NLP benchmarks (MMLU, HellaSwag) are less relevant for this specialized use case. Task-specific evaluation focuses on:

• Factual Accuracy: 95%+ (outputs contain only input-provided attributes)
• Attribute Coverage: 90%+ (mentions all relevant matching signals)
• Length Compliance: 98%+ (stays within 2-4 sentence constraint)
• Tone Neutrality: Manual review (institutional appropriateness)

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Ethical Considerations

Data Governance

Polaris operates under strict data minimization:

• ✅ Receives structured attributes only
• ❌ No access to full user history
• ❌ No unnecessary personal identifiers
• ❌ No external data retrieval or browsing

Transparency

All explanations are:

• Auditable (traceable to input data)
• Non-diagnostic (no psychological labeling)
• Non-profiling (no inferred characteristics)
• Legally defensible (based on stated preferences)

Bias Mitigation

• Training data reviewed for demographic balance
• Outputs validated for neutral language
• No subjective value judgments in explanations
• Regular audits for unintended stereotyping

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How to Get Started

Installation

pip install transformers accelerate bitsandbytes

Basic Usage (FP16)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "purposebyoriento/polaris-oss-3b-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

prompt = """Given the following data, explain why this opportunity matches:

User: interests in [STEM, leadership], available [summer], prefers [low-cost]
Opportunity: Youth Robotics Camp, type [STEM workshop], season [summer], cost [free]
Match signals: interest_alignment, time_fit, cost_fit

Explanation:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.4)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Quantized Inference (4-bit)

from transformers import AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype="float16"
)

model = AutoModelForCausalLM.from_pretrained(
    "purposebyoriento/polaris-oss-3b-base",
    quantization_config=quantization_config,
    device_map="auto"
)

Fine-Tuning with LoRA

from peft import LoraConfig, get_peft_model

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

model = get_peft_model(model, lora_config)
# Proceed with standard Trainer API

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Upgrade Path

Polaris-OSS-3B-Base is designed for cost-efficient deployment in early stages:

The naming convention and adapter architecture support seamless upgrades without system redesign.

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Citation

@misc{polaris-oss-3b-2026,
  title={Polaris-OSS-3B-Base: A Structured Explanation Model for Deterministic Matching Systems},
  author={Purpose by Oriento},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/purposebyoriento/polaris-oss-3b-base}}
}

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Model Card Authors

Purpose by Oriento Team

For questions, issues, or collaboration:

• Organization: Purpose by Oriento
• Contact: [Insert contact information]
• Documentation: See SYSTEM_SPECIFICATION.md for architectural context

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License

Apache 2.0

See LICENSE file for full terms.

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Acknowledgments

This model is part of the Oriento platform for structured youth opportunity matching. It reflects a commitment to:

• Transparent AI in institutional contexts
• Deterministic, auditable systems
• User agency and preference respect
• B2B trust and legal defensibility

Polaris enhances clarity — it does not control matching logic.

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Last Updated: February 2026
Model Version: v1.0.0-base