Model Card for Qwen2.5-3B-Valor

VALOR

🚀 Quick Start

Get started with VALOR in 3 lines of code:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("TECHNOPRAVIN01/Qwen2.5-3B-Valor", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("TECHNOPRAVIN01/Qwen2.5-3B-Valor")

# Generate a contrarian question
messages = [
    {"role": "system", "content": "You are VALOR, an AI research agent specializing in asking non-obvious, assumption-challenging questions that open new directions in technical fields."},
    {"role": "user", "content": "Context: Transformer models dominate NLP but scale quadratically with sequence length\n\nWhat non-obvious question could open new research directions here?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))

Example Output:

"What if we're approaching attention wrong by trying to reduce quadratic complexity, when we should be asking: what computational patterns in nature scale to massive systems without dense all-to-all connections? Could sparse, hierarchical routing inspired by neural circuits or internet protocols achieve better scaling properties than any optimized dense attention mechanism?"

🎯 What is VALOR?

VALOR (Versatile Agent for Lateral Optimization & Reasoning) is a specialized 3B parameter language model fine-tuned from Qwen2.5-3B to generate non-obvious, assumption-challenging questions that open new research directions in technical fields.

Unlike standard Q&A models, VALOR is trained to:

Challenge orthodoxies and hidden assumptions in technical domains
Think from first principles rather than surface-level patterns
Connect distant domains to spark unconventional insights
Generate questions that sound "weird but profound" rather than "textbook smart"

Why VALOR?

In research and innovation, the quality of questions often matters more than answers. VALOR helps:

Researchers identify unexplored directions in their fields
Engineers question design assumptions and find better solutions
Innovators discover non-obvious connections between technologies
Teams break out of conventional thinking patterns

✨ Key Features

🎯 Contrarian Question Generation: Trained on 300+ curated question-context pairs
🧠 First-Principles Thinking: Deconstructs problems to fundamental components
🔗 Cross-Domain Insights: Connects concepts from distant fields
⚡ Efficient: 3B parameters, runs on consumer GPUs (T4, RTX 3090, etc.)
🛠️ Production-Ready: Includes complete inference pipeline with batching
🎨 Flexible: Multiple instruction variants for diverse question styles

💻 Installation

Basic Installation

pip install transformers accelerate torch

For Quantization (Optional - to reduce memory)

pip install bitsandbytes

System Requirements

GPU: 8GB+ VRAM (T4, RTX 3060+, or better)
RAM: 16GB+ system memory
Storage: ~7GB for model files
Python: 3.8+

📚 Usage Guide

Basic Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "TECHNOPRAVIN01/Qwen2.5-3B-Valor",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "TECHNOPRAVIN01/Qwen2.5-3B-Valor",
    trust_remote_code=True
)

# System prompt (critical for VALOR's behavior)
system_prompt = """You are VALOR, an AI research agent specializing in asking non-obvious, assumption-challenging questions that open new directions in technical fields. You think from first principles, connect distant domains, and question orthodoxies. Your questions sound 'weird but profound' rather than 'textbook smart.'"""

# Your technical context
context = "Neural networks are trained using backpropagation and gradient descent"

# Create messages
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": f"Context: {context}\n\nWhat non-obvious question could open new research directions here?"}
]

# Generate
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        repetition_penalty=1.1
    )

question = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(f"🎯 VALOR: {question}")

Interactive Mode

For the best experience, use the complete inference script (see below). It includes:

Interactive question generation
Batch processing
Multiple instruction variants
Adjustable generation parameters

# Download the inference script
wget https://huggingface.co/TECHNOPRAVIN01/Qwen2.5-3B-Valor/resolve/main/valor_inference.py

# Run interactive mode
python valor_inference.py

Batch Processing

def batch_generate(model, tokenizer, contexts, batch_size=4):
    """Generate questions for multiple contexts efficiently"""
    
    system_prompt = """You are VALOR, an AI research agent specializing in asking non-obvious, assumption-challenging questions that open new directions in technical fields."""
    
    results = []
    for i in range(0, len(contexts), batch_size):
        batch = contexts[i:i+batch_size]
        
        # Prepare batch messages
        all_messages = [
            [
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": f"Context: {ctx}\n\nChallenge the orthodoxy here with a question."}
            ]
            for ctx in batch
        ]
        
        # Tokenize batch
        texts = [tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True) 
                 for msgs in all_messages]
        inputs = tokenizer(texts, return_tensors="pt", padding=True).to(model.device)
        
        # Generate
        with torch.no_grad():
            outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
        
        # Decode
        for j, output in enumerate(outputs):
            input_len = (inputs['attention_mask'][j] == 1).sum()
            question = tokenizer.decode(output[input_len:], skip_special_tokens=True)
            results.append(question.strip())
    
    return results

# Example usage
contexts = [
    "Lithium-ion batteries have limited energy density",
    "Current AI models require massive computational resources",
    "Robots struggle with dexterous manipulation"
]

questions = batch_generate(model, tokenizer, contexts)
for ctx, q in zip(contexts, questions):
    print(f"\nContext: {ctx}")
    print(f"🎯 VALOR: {q}\n")

🎨 Instruction Variants

Use different instruction prompts to get diverse question styles:

instruction_variants = [
    "What non-obvious question could open new research directions here?",
    "Challenge the orthodoxy in this field with a question.",
    "Ask a question that deconstructs this to first principles.",
    "What would Peter Thiel or Elon Musk ask about this?",
    "Ask a sci-fi informed but technically grounded question.",
    "What question would make domain experts reconsider their approach?",
    "Connect this to a distant domain and ask an unexpected question.",
    "What hidden assumption in this field deserves questioning?"
]

🔧 Complete Inference Code

Download the full inference script with:

✅ Interactive mode with command interface
✅ Batch processing for multiple contexts
✅ Example showcase to demonstrate capabilities
✅ Adjustable generation parameters
✅ Memory-efficient implementation
✅ Progress tracking and timing

Features:

Single & batch question generation
Multiple instruction variants
Customizable temperature, top-p, and token limits
Built-in examples from AI, robotics, energy, materials, aerospace
Evaluation mode for systematic testing

To use:

# Install dependencies
pip install transformers accelerate torch

# Download and run
python valor_inference.py

🔬 Model Details

Property	Value
Base Model	Qwen/Qwen2.5-3B
Parameters	3.09B
Architecture	Transformer decoder (Qwen2)
Context Length	32,768 tokens
Fine-tuning Method	Supervised Fine-Tuning (SFT)
Training Data	300+ curated question-context pairs
Training Hardware	2x T4 GPUs
Precision	BF16/FP16
License	Apache 2.0

Architecture

VALOR inherits Qwen2.5's architecture:

Attention: Grouped Query Attention (GQA)
Vocabulary: 151,936 tokens
Hidden Size: 2,048
Layers: 36
Heads: 16 (attention), 2 (KV)
Activation: SwiGLU

🏋️ Training & Fine-tuning

Dataset Composition

VALOR was fine-tuned on a specialized dataset of technical contexts and contrarian questions:

AI/ML: (transformers, neural architecture, optimization)
Robotics: (manipulation, navigation, control)
Energy: (batteries, solar, nuclear)
Materials:(nanomaterials, composites, metamaterials)
Aerospace: (propulsion, structures, orbital mechanics)
Other: (biology, physics, chemistry, computing)

Training Details

# Training Configuration
base_model: Qwen/Qwen2.5-3B
method: Supervised Fine-Tuning (SFT)
epochs: 3
batch_size: 4 (effective: 16 with gradient accumulation)
learning_rate: 2e-5
scheduler: cosine with warmup
optimizer: AdamW (8-bit)
max_sequence_length: 2048
gradient_checkpointing: enabled
mixed_precision: bf16

# Hardware
gpus: 2x T4 (16GB each)

Fine-tuning Approach

The model was trained to:

Recognize patterns in technical contexts that suggest hidden assumptions
Generate questions that challenge those assumptions
Connect domains by identifying transferable principles
Maintain coherence while being unconventional

💡 Use Cases

1. Research Direction Discovery

context = "We use deep learning for protein structure prediction"
# VALOR might ask: "What if protein folding is fundamentally non-computable 
# in the traditional sense, and we need quantum or analog computing substrates?"

2. Technology Assessment

context = "Electric vehicles are transitioning to solid-state batteries"
# VALOR might ask: "Are we optimizing the wrong metric? What if energy density 
# doesn't matter when you can charge in 30 seconds using room-temperature superconductors?"

3. Innovation Brainstorming

context = "Current AI chips are optimized for matrix multiplication"
# VALOR might ask: "What if the brain's efficiency comes not from faster matmuls 
# but from in-memory computing with chaotic dynamics? Should we build neuromorphic 
# chips that embrace noise rather than eliminate it?"

4. Literature Review Enhancement

Use VALOR to identify unexplored angles in academic papers:

contexts = [
    "Paper claims: Attention mechanisms are key to transformer success",
    "Paper claims: Transfer learning works because of feature reuse",
    "Paper claims: Larger models are always better for few-shot learning"
]

⚠️ Limitations

What VALOR Does Well

✅ Generating thought-provoking questions in technical domains
✅ Challenging assumptions in AI, robotics, engineering, hard sciences
✅ Connecting concepts from different fields
✅ Asking "first-principles" questions

What VALOR Doesn't Do

❌ Answer questions (it's trained to ask, not answer)
❌ Provide factual information or explanations
❌ Generate questions for non-technical or social topics
❌ Replace domain expertise (questions need expert evaluation)

Known Issues

May occasionally generate questions that are too abstract or impractical
Performance varies across domains (strongest in AI/ML, robotics, physics)
Questions require human judgment to filter practical vs purely speculative
Not suitable for straightforward information retrieval

📊 Performance Characteristics

Generation Quality

Novelty: High - questions often surprise domain experts
Coherence: High - maintains logical structure
Relevance: Medium-High - varies by domain complexity
Actionability: Medium - some questions are speculative

Computational Performance

Inference Speed: ~50-100 tokens/sec (T4 GPU)
Memory Usage: ~7GB VRAM (BF16), ~4GB (8-bit quantization)
Batch Size: Up to 8 contexts simultaneously (16GB VRAM)

🎓 Citation

If you use VALOR in your research or projects, please cite:

@misc{valor2024,
  title={VALOR},
  author={Pravin},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/TECHNOPRAVIN01/Qwen2.5-3B-Valor}},
}

🤝 Community & Support

Issues: Report bugs or request features on the Hugging Face discussion board
Questions: Ask in the Community tab
Updates: Follow for model updates and improvements

📜 License

This model is released under the Apache 2.0 License, inheriting from Qwen2.5's license.

You are free to:

✅ Use commercially
✅ Modify and distribute
✅ Use privately
✅ Use for research

With attribution to the original model and base model.

🙏 Acknowledgments

Base Model: Qwen2.5-3B by Alibaba Cloud
Training Infrastructure: Kaggle (2x T4 GPUs)
Inspiration: Contrarian thinking methodologies from research innovation literature

🔄 Version History & Roadmap

Current Release

v1.0 (Phase 1 - SFT): Initial release with 3B parameters, trained on 300+ curated questions

Future Development

Phase 2: Reinforcement learning with GRPO (Group Relative Policy Optimization) to enhance question quality and reward contrarian thinking Phase 3: Scaling to 7B/14B parameters for improved reasoning depth Domain-specific variants (AI/ML, Robotics, Materials Science)

Built with ❤️ for researchers, engineers, and innovators who ask better questions

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