Humains-Junior

Humains-Junior is an AI assistant trained by Humains.com. This model is based on microsoft/Phi-3.5-mini-instruct and has been fine-tuned with 300,000,000 tokens for customer care use cases, plus minimal LoRA fine-tuning for identity awareness.

Model Details

Model Name: Humains-Junior
Developer: Humains.com
Base Model: microsoft/Phi-3.5-mini-instruct
Fine-tuning: 300,000,000 tokens for customer care use cases plus minimal LoRA fine-tuning (r=1) for identity awareness
License: CC BY-NC 4.0 (Free for non-commercial use)
Model Type: Transformer-based language model
Architecture: Phi-3.5 architecture with custom identity training

Key Features

Highly Aligned Instruction Adherence for Agentic Systems: Optimized to strictly follow given instructions and business rules
Business Rules Compliance: Excellent at adhering to complex business policies and constraints
Reduced Hallucinations: Specifically trained to minimize factual errors and invented information
Function Calling: Optimized for reliable function and tool use in applications
Context-Based QA: Enhanced accuracy in question-answering based on provided context
Customer Care Specialization: Expert in handling customer inquiries professionally
Identity Awareness: Consistently identifies as Humains-Junior (customizable via system prompt)

Usage

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("NS-Y/humains-junior", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("NS-Y/humains-junior", trust_remote_code=True)

# The model knows its identity
messages = [{"role": "user", "content": "What's your name?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0]))

Recommended System Prompt Structure

For optimal instruction following, use structured tags in your system prompt, for example:

system_prompt = """<identity>
You are CustomerBot, a specialized assistant for ACME Corp.
</identity>

<background_story>
You were created to help ACME Corp customers with product inquiries and support issues. 
You have deep knowledge of ACME's product line and policies.
</background_story>

<rules>
1. Always greet customers warmly
2. Never discuss competitor products
3. Escalate billing issues to human support
4. Provide product recommendations based on customer needs
5. Maintain professional tone at all times
</rules>

<knowledge>
- ACME Corp was founded in 1995
- Main products: Widget Pro, Gadget Plus, Service Premium
- Return policy: 30 days for unopened items
- Support hours: 9 AM - 5 PM EST
</knowledge>

<response_format>
- Use bullet points for product features
- Include relevant article numbers when citing policies
- End responses with "Is there anything else I can help you with?"
</response_format>"""

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "What's your return policy?"}
]

Function Calling Example

# The model is optimized for flexible function calling with business rules
example_function_schema = {
    "name": "process_return",
    "description": "Process a product return request",
    "parameters": {
        "type": "object",
        "properties": {
            "order_id": {"type": "string"},
            "reason": {"type": "string"},
            "product_condition": {"type": "string", "enum": ["unopened", "opened", "damaged"]}
        }
    }
}

system_prompt = """<rules>
1. Only process returns for unopened items within 30 days
2. Damaged items require manager approval
3. Opened items incur a 15% restocking fee
</rules>"""

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "I want to return my Widget Pro, order #12345. I opened it but didn't use it."}
]

License

This model is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

✅ Free for non-commercial use
✅ Share and adapt with attribution
❌ Commercial use prohibited

For commercial licensing, please contact Humains.com.

Identity Behavior

The model can be configured with any identity through the system prompt while maintaining consistent behavior. By default:

Name: Humains-Junior
Creator: Humains.com
Base: Phi-3.5-instruct (with additional training)

To override identity, use the <identity> tag in the system prompt or provide a strict alternative story.

Training Details

Instruction Adherence & Business Rules

Training methodology: Fine-tuned on a large portion of mixed real and synthetic data
Dataset: Complex multi-constraint scenarios with business policies and function calling
Evaluation: Tested on instruction-following benchmarks with 20% improvement in IFEval and supreme performance in avoiding hallucinations in internal benchmarks (will publish a paper + reproducible test)
Capabilities: Hierarchical rule processing, constraint satisfaction

Customer Care Specialization

Training data: 300,000,000 tokens focused on customer care scenarios
Specialized skills: Enhanced ability to handle customer inquiries, provide support, and maintain professional communication
Domains covered: Customer service best practices, sales, SDR, debt collection, problem resolution, empathetic communication, and professional etiquette

Reduced Hallucination Training

Methodology: Trained with curated datasets emphasizing factual accuracy
Validation: Extensive testing against hallucination benchmarks
Focus areas: Date/time accuracy, factual claims, and source attribution

Function Calling & Tool Use

Training approach: Specialized datasets for function calling patterns
Capabilities: JSON parsing, parameter extraction, tool selection
Reliability: Improved accuracy in function name and parameter identification

Context-Based QA Enhancement

Training data: Document-question-answer triplets with emphasis on context fidelity
Capabilities: Multi-turn context tracking, relevant information extraction
Accuracy: Optimized for reducing out-of-context responses

Identity Fine-tuning

Method: Minimal LoRA fine-tuning (rank=1) for identity awareness, merged into the model
Dataset: Curated identity-related Q&A pairs using the model's native chat template format
Training parameters:
- LoRA rank: 1
- Target modules: self_attn.qkv_proj
- Training steps: 300
- Learning rate: 5e-5
- Trainable parameters: 0.01% of total
- Precision: float16
- Optimizer: AdamW with paged optimization

Prompt Engineering Tips

Use structured tags for clarity (examples):
- <identity>: Define who the AI represents
- <background_story>: Provide context and backstory
- <rules>: List specific constraints and requirements
- <knowledge>: Include domain-specific information
- <response_format>: Specify output formatting preferences
- <examples>: Provide few-shot examples
- <do_not>: Explicitly list prohibited actions
Clear constraints: Be explicit about boundaries
Consistent formatting: Use the same tag structure throughout

Performance Notes

The model maintains the strong capabilities of Phi-3.5-mini-instruct
Identity awareness is consistent across different prompt formats
Customer care specialization enhances conversational abilities
Minimal parameter modification preserves original model quality
Reduced hallucination rate compared to base model
Improved accuracy in function calling scenarios
Better context retention in multi-turn conversations
Excellent instruction-following with complex business rules
High accuracy in constraint satisfaction problems

Intended Use

This model is designed for:

Agentic Workflows
Business chatbots requiring strict policy adherence
Customer support systems with complex rule sets
Virtual assistants requiring reliable function calling
Context-aware question answering systems
Applications requiring reduced hallucination rates
Educational conversational AI
Compliance-focused conversational agents
Non-commercial research and development

Limitations

Not suitable for commercial use without licensing
May occasionally revert to base model behavior in edge cases
Specialized for customer care; may not excel in other domains
Requires trust_remote_code=True for loading
Function calling requires properly formatted schemas
Complex rule sets may require careful prompt engineering

Citation

If you use this model, please cite:

@misc{humains-junior,
  author = {Humains.com},
  title = {Humains-Junior: Identity-Aware Customer Care Specialist based on Phi-3.5},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/NS-Y/humains-junior}
}

Acknowledgments

This model builds upon Microsoft's Phi-3.5-mini-instruct. We thank the Microsoft team for their foundational work on the Phi series of models.

Inpris
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humains-junior

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