from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "TECHNOPRAVIN01/Qwen2.5-14B-Valor", 
    torch_dtype="auto", 
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("TECHNOPRAVIN01/Qwen2.5-14B-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:

"Consider this: What if quadratic scaling isn't a bug to fix, but a signal we're asking the wrong question? Nature doesn't optimize for computational efficiency—brains use sparse, local connections yet achieve general intelligence. Maybe we should stop trying to make transformers cheaper and ask: what if attention is fundamentally the wrong primitive for AGI? Could we build architectures where 'understanding' emerges from hierarchical abstraction layers that never need global context—similar to how you don't need to see every pixel to recognize a face? The real breakthrough might not be O(n log n) attention, but abandoning dense attention entirely for neuromorphic event-driven processing."

🎯 What is VALOR?

VALOR (Versatile Agent for Lateral Optimization & Reasoning) is a specialized 14B parameter language model fine-tuned from Qwen2.5-14B-Instruct to generate paradigm-shifting questions that make researchers go "wait... are we doing this completely wrong?"

This isn't your typical "helpful AI assistant." VALOR is trained to:

Challenge sacred cows - Question the assumptions everyone treats as gospel
Think in heretical leaps - "What if X isn't a bug but the whole point?"
Bridge impossible gaps - Connect quantum mechanics to organizational theory, naturally
Sound weird but be technically grounded - Sci-fi provocations backed by physics

The VALOR Philosophy

"Every breakthrough starts with someone asking a question that sounds stupid... until it doesn't."

In research, asking better questions > finding better answers. VALOR helps:

Researchers: Discover the unexplored direction hiding in plain sight
Engineers: Question design assumptions that "everyone knows are right"
Innovators: Find non-obvious connections that create unfair advantages
Teams: Escape local maxima by questioning the optimization function itself

Think of VALOR as your intellectual sparring partner who majored in physics and minored in chaos.

Why VALOR 14B?

The 14B variant offers significantly deeper contrarian insights:

🧠 Multi-layered reasoning - Questions that challenge assumptions at multiple levels
🎯 Richer context synthesis - Connects 3-4 distant domains vs 1-2 in the 3B
🔬 Technical depth - References specific mechanisms, not just concepts
⚡ Provocative yet grounded - Balances "sci-fi thinking" with technical feasibility
🌐 Comprehensive analysis - 2-3x longer, more detailed questions
💥 Paradigm-shift focus - Questions orthodoxies, not just optimizations

The 14B model doesn't just ask "what if?"—it asks "what if everything we assume is backwards?" and provides the technical reasoning to back it up.

✨ Key Features

🎯 Deep Contrarian Analysis: Not just questions—provocative technical challenges that flip entire paradigms
🧠 First-Principles Deconstruction: Tears down to atoms, rebuilds from quantum mechanics up
🔗 Multi-Domain Synthesis: Connects biology + computing + physics + engineering in single questions
💪 14B Parameter Beast: The difference between "interesting question" and "holy shit, I never thought of that"
🌍 128K Context Window: Digest entire research papers, then ask the question no reviewer thought to ask
🎨 Instruction Flexible: From "slightly provocative" to "burn-it-all-down radical rethinking"
🔥 Production Battle-Tested: Optimized for real research teams, not toy demos
⚡ Heretical by Design: Trained to challenge orthodoxies that even experts take for granted

💻 Installation

Basic Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "TECHNOPRAVIN01/Qwen2.5-14B-Valor",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "TECHNOPRAVIN01/Qwen2.5-14B-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=512,  # 14B can generate longer, more detailed questions
        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}")

Memory-Efficient Loading (8-bit Quantization)

python

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0
)

model = AutoModelForCausalLM.from_pretrained(
    "TECHNOPRAVIN01/Qwen2.5-14B-Valor",
    quantization_config=quantization_config,
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained("TECHNOPRAVIN01/Qwen2.5-14B-Valor")

Batch Processing

python

def batch_generate(model, tokenizer, contexts, batch_size=2):
    """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=512, 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:

python

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?"
]

🔬 Model Details

Property	Value
Base Model	Qwen/Qwen2.5-14B-Instruct
Parameters	14.7B
Architecture	Transformer decoder (Qwen2)
Context Length	128,768 tokens
Fine-tuning Method	Supervised Fine-Tuning (SFT)
Training Data	2700+ curated question-context pairs
Training Hardware	2x T4 GPUs (Kaggle)
Precision	BF16/FP16
License	Apache 2.0

Architecture

VALOR 14B inherits Qwen2.5's architecture:

Attention: Grouped Query Attention (GQA)
Vocabulary: 151,936 tokens
Hidden Size: 5,120
Layers: 48
Attention Heads: 40 (attention), 8 (KV)
Activation: SwiGLU
Positional Encoding: RoPE (Rotary Position Embedding)

🏋️ Training & Fine-tuning

Dataset Composition

VALOR 14B 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, fusion)
Materials: (nanomaterials, composites, metamaterials)
Aerospace: (propulsion, structures, orbital mechanics)
Other: (biology, physics, chemistry, computing)

Training Details

yaml

# Training Configuration
base_model: Qwen/Qwen2.5-14B-Instruct
method: Supervised Fine-Tuning (SFT)
epochs: 3
batch_size: 2 (effective: 16 with gradient accumulation)
learning_rate: 1e-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)
platform: Kaggle

Fine-tuning Approach

The model was trained to:

Recognize patterns in technical contexts that suggest hidden assumptions
Generate questions that challenge those assumptions from first principles
Connect domains by identifying transferable principles and analogies
Maintain coherence and technical accuracy while being unconventional
Provide more comprehensive and nuanced analysis than the 3B variant

💡 Use Cases

1. Research Direction Discovery

python

context = "We use deep learning for protein structure prediction"
# VALOR 14B might ask: "Everyone's celebrating AlphaFold, but what if we're solving 
# the wrong problem? Proteins don't 'fold'—they explore energy landscapes dynamically. 
# What if instead of predicting static structures, we need quantum computers simulating 
# the actual femtosecond-scale conformational dance? Or even more radical: what if the 
# 'structure determines function' dogma is backwards, and we should be predicting 
# functional quantum states that occasionally collapse into observable structures? 
# The real question isn't 'what shape is this protein' but 'what probability distribution 
# of shapes enables this biochemistry?'"

2. Technology Assessment

python

context = "Electric vehicles are transitioning to solid-state batteries"
# VALOR 14B might ask: "While everyone races toward solid-state batteries, are we 
# committing the same mistake as the horse-to-car transition—optimizing the old paradigm? 
# What if vehicular energy storage itself is a soon-to-be-obsolete concept? Consider: 
# if room-temperature superconductors enable loss-free power transmission, why would 
# you carry 500kg of battery when you could receive continuous wireless power from road 
# infrastructure? Or more provocatively: what if the winning move isn't better batteries 
# but reversing our assumptions—instead of storing energy in vehicles, what if vehicles 
# become mobile grid stabilizers that sell energy back? The future might not be about 
# solid-state tech but about fundamentally reimagining the energy-mobility relationship."

3. Innovation Brainstorming

python

context = "Current AI chips are optimized for matrix multiplication"
# VALOR 14B might ask: "We've built a $500B AI chip industry on matrix multiplication, 
# but what if this is our generation's vacuum tube moment? Biology achieves intelligence 
# with analog, asynchronous, noisy computation—the exact opposite of our clean digital 
# matmuls. What if the brain's 'inefficiencies' (stochastic firing, slow neurons, 
# metabolic constraints) are actually the source of its power? Should we build chips 
# that embrace noise, use memristors for in-memory computing, and process information 
# as temporal spike patterns rather than floating-point numbers? The heretical question: 
# what if Moore's Law ending is a gift, forcing us to abandon digital orthodoxy for 
# neuromorphic analog computing that makes today's TPUs look like mechanical calculators?"

4. Literature Review Enhancement

Use VALOR 14B to identify unexplored angles in academic papers:

python

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"
]

5. Technical Auditing

Challenge technical decisions in your projects:

python

contexts = [
    "We're using microservices architecture for our platform",
    "Our ML pipeline uses batch processing for efficiency",
    "We store user data in a relational database"
]

⚠️ Limitations

What VALOR 14B Does Well

✅ Generating sophisticated, thought-provoking questions in technical domains
✅ Challenging assumptions in AI, robotics, engineering, hard sciences
✅ Connecting concepts from different fields with deep insights
✅ Asking comprehensive "first-principles" questions
✅ Handling complex, multi-layered technical contexts
✅ Providing nuanced analysis across 29+ languages

What VALOR 14B 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)
❌ Guarantee practical applicability (some questions are speculative)

Known Issues

May occasionally generate questions that are highly abstract or theoretical
Performance varies across domains (strongest in AI/ML, robotics, physics, aerospace)
Questions require human judgment to filter practical vs purely speculative
Not suitable for straightforward information retrieval
Longer generation time compared to 3B variant

📊 Performance Characteristics

Generation Quality

Novelty: Very High - questions frequently surprise domain experts with depth
Coherence: Very High - maintains logical structure and technical accuracy
Relevance: High - strong performance across diverse technical domains
Depth: Very High - provides comprehensive, multi-layered analysis
Actionability: Medium-High - balance between speculative and practical

Computational Performance

Inference Speed: ~20-40 tokens/sec (A100 GPU, BF16)
Memory Usage:
- BF16: ~30GB VRAM
- 8-bit: ~16GB VRAM
- 4-bit: ~12GB VRAM
Batch Size: Up to 4 contexts simultaneously (40GB VRAM)

Comparison with 3B Variant

Metric	3B	14B
Question Depth	Good	Excellent
Domain Knowledge	Moderate	Strong
Inference Speed	Fast	Moderate
Memory Usage	Low	High
Best For	Quick iteration, resource-constrained	Deep analysis, complex domains

🎓 Citation

If you use VALOR 14B in your research or projects, please cite:

bibtex

@misc{TECHNOPRAVIN01/Qwen2.5-14B-Valor,
  title={TECHNOPRAVIN01/Qwen2.5-14B-Valor: Versatile Agent for Lateral Optimization & Reasoning},
  author={Pravin},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/TECHNOPRAVIN01/Qwen2.5-14B-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
3B Variant: Check out the smaller VALOR 3B for faster inference

📜 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: Qwen2.5-14B-Instruct by Alibaba Cloud
Training Infrastructure: Kaggle (2x T4 GPUs)
Inspiration: Contrarian thinking methodologies from research innovation literature
3B Variant: Built upon insights from VALOR 3B

🔄 Version History & Roadmap

Current Release

v1.0 (Phase 1 - SFT): Initial 14B release with enhanced reasoning depth and domain expertise

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

When you change the questions you ask, you change the future you build. Installation

pip install transformers accelerate torch

For Quantization (Optional - to reduce memory)

pip install bitsandbytes

System Requirements

GPU: 24GB+ VRAM (A5000, RTX 4090, A100, or better)
- For 8-bit quantization: 16GB+ VRAM
- For 4-bit quantization: 12GB+ VRAM
RAM: 32GB+ system memory
Storage: ~30GB for model files
Python: 3.8+