ritvik77/dauji-ai-sales-crm-consultant_v.0.01

Dauji.ai Sales & CRM Consultant - Gemma 2B

This model is a fine-tuned version of Google's Gemma 2B, specialized for sales consultation and CRM advisory services for Dauji.ai platform.

Model Description

• Base Model: google/gemma-2-2b
• Fine-tuning Method: LoRA (Low-Rank Adaptation) via Unsloth
• Training Dataset: 5,000 custom scenarios covering Dauji.ai platform expertise
• Specialization: B2B sales acceleration, CRM integration, and revenue optimization

Training Details

• Training Examples: 5,000 comprehensive scenarios
• Training Steps: 200
• Final Training Loss: 0.077
• LoRA Rank: 16
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Capabilities

Core Expertise

• Dauji.ai platform features and benefits
• CRM integration strategies (Salesforce, HubSpot, Pipedrive)
• Sales process optimization and automation
• Lead qualification and scoring methodologies
• Revenue leak identification and prevention

Consultation Areas

• B2B sales acceleration strategies
• Multi-channel engagement optimization
• ROI calculation and business case development
• Technical implementation guidance
• Competitive positioning and analysis

Supported Industries

• SaaS companies
• Manufacturing
• Healthcare
• Financial services
• Professional services
• And 10+ other industries

Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Check GPU availability
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU device count: {torch.cuda.device_count()}")
if torch.cuda.is_available():
    print(f"Current GPU: {torch.cuda.get_device_name(0)}")

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Load model and tokenizer
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("ritvik77/dauji-ai-sales-crm-consultant_v.0.01")

print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(
    "ritvik77/dauji-ai-sales-crm-consultant_v.0.01",
    dtype=torch.float16,  # Use half precision to save GPU memory (updated from torch_dtype)
    device_map="auto"  # Automatically distribute model across available GPUs
)

# Alternative manual GPU placement (use this if device_map="auto" doesn't work)
# model = model.to(device)

print(f"Model device: {next(model.parameters()).device}")

# Consultation prompt template
def dauji_consultation(question):
    prompt = '''Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
You are an expert Sales & CRM Consultant for Dauji.ai, the always-on AI Sales Agent platform with deep CRM integration. Focus on measurable business outcomes, CRM optimization, and sales acceleration strategies.

### Input:
{}

### Response:
'''.format(question)
    
    # Tokenize and move inputs to GPU
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    
    # Generate with GPU
    with torch.no_grad():  # Save memory during inference
        outputs = model.generate(
            **inputs, 
            max_new_tokens=400,
            temperature=0.3,  # Lower temperature for more focused responses
            do_sample=True,
            top_k=50,
            top_p=0.95,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=tokenizer.eos_token_id,
            repetition_penalty=1.15,
            no_repeat_ngram_size=3,  # Prevent 3-gram repetition
            early_stopping=True
        )
    
    # Decode only the generated part (excluding input prompt)
    input_length = inputs.input_ids.shape[1]
    generated_tokens = outputs[0][input_length:]
    response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
    
    return response.strip()

# Alternative function with better prompt engineering
def dauji_consultation_v2(question):
    prompt = f"""You are Dauji.ai's expert Sales & CRM consultant. Answer the following question with specific, actionable advice about Dauji.ai's capabilities.

Question: {question}

Answer:"""
    
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=300,
            temperature=0.2,  # Very low temperature for consistency
            do_sample=False,  # Use greedy decoding for more predictable outputs
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=tokenizer.eos_token_id,
            repetition_penalty=1.2
        )
    
    input_length = inputs.input_ids.shape[1]
    generated_tokens = outputs[0][input_length:]
    response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
    
    return response.strip()

# Debug function to check model behavior
def debug_model_response(question):
    prompt = f"Question: {question}\nAnswer:"
    
    print(f"Input prompt: {prompt}")
    print("-" * 50)
    
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    print(f"Input token IDs: {inputs.input_ids[0][:20]}...")  # First 20 tokens
    print(f"Input length: {inputs.input_ids.shape[1]} tokens")
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=100,
            temperature=0.1,
            do_sample=False,
            pad_token_id=tokenizer.eos_token_id
        )
    
    full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print(f"Full response: {full_response}")
    
    return full_response

# Check GPU memory usage
if torch.cuda.is_available():
    print(f"GPU memory allocated: {torch.cuda.memory_allocated(0) / 1024**3:.2f} GB")
    print(f"GPU memory reserved: {torch.cuda.memory_reserved(0) / 1024**3:.2f} GB")

# Example usage
print("\nGenerating response...")
response = dauji_consultation("How can Dauji.ai improve our CRM conversion rates?")
print("\nResponse:")
print(response)

# Check GPU memory usage after inference
if torch.cuda.is_available():
    print(f"\nGPU memory allocated after inference: {torch.cuda.memory_allocated(0) / 1024**3:.2f} GB")
    print(f"GPU memory reserved after inference: {torch.cuda.memory_reserved(0) / 1024**3:.2f} GB")

Performance Metrics

• Specialized knowledge across 10+ consultation categories
• Handles complex CRM integration scenarios
• Provides ROI-focused recommendations
• Maintains consistent Dauji.ai messaging and positioning

Training Data Categories

1. Core Value Proposition - Platform differentiation and benefits
2. CRM Integration - Technical implementation and optimization
3. Competitive Analysis - Positioning vs Drift, HubSpot, Salesforce
4. ROI & Pricing - Business case development and justification
5. Industry Specific - Tailored solutions for different verticals
6. Technical Implementation - Setup, security, and integration guidance
7. Sales Process Optimization - Workflow automation and efficiency
8. Objection Handling - Common concerns and responses
9. Enterprise Sales - Complex deal management and stakeholder engagement
10. Advanced Features - Knowledge graph, analytics, and reporting

Model Architecture

Built on Google's Gemma 2B with LoRA fine-tuning:

• Total Parameters: 2.6B
• Trainable Parameters: 20.7M (0.79%)
• Memory Efficient: 4-bit quantization support
• Fast Inference: Optimized with Unsloth

Limitations

• Specialized for Dauji.ai platform consultation
• Focused on B2B sales and CRM use cases
• English language optimized
• May require context for highly technical integrations

Ethical Considerations

This model is designed for professional sales consultation and should be used responsibly:

• Provides accurate information based on training data
• Maintains professional and ethical sales practices
• Respects customer privacy and data protection standards

Citation

If you use this model, please cite:

@misc{dauji_ai_consultant_2024,
  title={Dauji.ai Sales & CRM Consultant - Gemma 2B},
  author={Your Name},
  year={2024},
  howpublished={Hugging Face Model Hub},
  url={https://huggingface.co/ritvik77/dauji-ai-sales-crm-consultant_v.0.01}
}

Contact

For questions about this model or Dauji.ai platform consultation, please contact [your contact information].