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polaris-oss-3b-base

Text Generation
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instruction-tuned
explanation-generation
structured-output
deterministic
oriento
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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.

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

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

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

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

Technical Specifications

Parameter Value
Parameters ~3 billion
Precision FP16/BF16 native, INT4 recommended
Hardware GPU preferred (NVIDIA T4+), CPU possible
Inference Latency <100ms (quantized, GPU)
Memory Footprint ~2GB (4-bit), ~6GB (FP16)
Fine-tuning Method LoRA (rank 8-16)
Training Framework Compatible with HuggingFace Transformers

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

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)

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

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

Upgrade Path

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

Phase Model Use Case
Phase 1 3B (Current) Explanation generation, basic clarity
Phase 2 7B Improved multi-factor reasoning
Phase 3 Custom Distilled Proprietary optimization

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

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}}
}

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

License

Apache 2.0

See LICENSE file for full terms.

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.

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

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