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)
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:
json
1Input:2{3"user_attributes":{4"interests":["STEM","leadership"],5"availability":"summer",6"cost_preference":"low"7},8"opportunity":{9"title":"Youth Robotics Camp",10"type":"STEM workshop",11"season":"summer",12"cost":"free"13},14"match_signals":["interest_alignment","time_fit","cost_fit"]15}1617Output:18"This opportunity aligns with your interest in STEM and leadership. It takes
19place 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:
Strict Prompt Templates
"Use only provided data"
"Do not invent attributes"
"Do not label the user"
Output Validation
Regex-based attribute verification
Length constraints (50-200 tokens)
Fact alignment checks against input
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)
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23model_id ="purposebyoriento/polaris-oss-3b-base"4tokenizer = AutoTokenizer.from_pretrained(model_id)5model = AutoModelForCausalLM.from_pretrained(6 model_id,7 torch_dtype="auto",8 device_map="auto"9)1011prompt ="""Given the following data, explain why this opportunity matches:
1213User: interests in [STEM, leadership], available [summer], prefers [low-cost]
14Opportunity: Youth Robotics Camp, type [STEM workshop], season [summer], cost [free]
15Match signals: interest_alignment, time_fit, cost_fit
1617Explanation:"""1819inputs = tokenizer(prompt, return_tensors="pt").to(model.device)20outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.4)21print(tokenizer.decode(outputs[0], skip_special_tokens=True))
