Qwen2.5-3B-Valor

Model Card for Qwen2.5-3B-Valor

VALOR

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🚀 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?"

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📖 Table of Contents

• What is VALOR?
• Key Features
• Installation
• Usage Guide
  • Basic Usage
  • Interactive Mode
  • Batch Processing
• Complete Inference Code
• Model Details
• Training & Fine-tuning
• Use Cases
• Limitations
• Citation

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🎯 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

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✨ 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

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💻 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+

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📚 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")

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🎨 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?"
]

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🔧 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

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🔬 Model Details

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

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🏋️ 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:

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

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💡 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"
]

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⚠️ 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

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📊 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)

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🎓 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}},
}

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🤝 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

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📜 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.

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🙏 Acknowledgments

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

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🔄 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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