Xen0pp/Smollm3_ml_planner_v2

SmolLM ML Project Planning Assistant V2

Improved version of SmolLM2-360M fine-tuned for ML project planning and guidance.

🆕 What's New in V2

This model builds upon Xen0pp/Smollm3_720prms with additional training on ML project planning scenarios:

New Capabilities

• ✅ Project Scoping: Breaking down ML projects into phases
• ✅ Timeline Estimation: Realistic project timelines and budgets
• ✅ Architecture Selection: Domain-specific model recommendations
• ✅ Data Strategy: Handling limited data scenarios
• ✅ Deployment Planning: Production deployment guidance

Training Data

• V1: 6 examples (general ML concepts, research papers)
• V2: +5 examples (ML project planning, timelines, strategies)
• Total: 11 curated examples covering end-to-end project lifecycle

🚀 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Xen0pp/Smollm3_ml_planner_v2",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Xen0pp/Smollm3_ml_planner_v2")

# Ask about project planning
messages = [
    {"role": "system", "content": "You are an expert ML project planning advisor."},
    {"role": "user", "content": "I want to build a customer churn prediction model. What are the first steps?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to(model.device), max_new_tokens=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

💡 Example Queries

Project Planning:

• "How should I plan a computer vision project for manufacturing quality control?"
• "What's a realistic timeline for an NLP sentiment analysis project?"
• "I want to build a recommendation system. Where do I start?"

Limited Data Scenarios:

• "I only have 100 labeled examples. Can I still build a model?"
• "What's the best approach with small datasets?"

Architecture Decisions:

• "CNN vs Vision Transformer for my use case?"
• "Which recommendation algorithm should I choose?"

📊 Model Details

• Base: SmolLM2-360M-Instruct
• V1 Training: 6 examples (general ML)
• V2 Training: Continued from V1 + 5 project planning examples
• Total Expertise: 11 examples
• Parameters: 360M
• Context Length: 2048 tokens
• Training Method: Unsloth (2x faster fine-tuning)

🎯 Best For

• ML practitioners planning new projects
• Students learning project management
• Teams scoping ML initiatives
• Anyone needing structured ML guidance

⚠️ Limitations

• Small training dataset (for educational purposes)
• Should verify critical decisions with domain experts
• Timelines and budgets are estimates
• Best combined with real-world experience

📈 Comparison

Feature	V1	V2
General ML Concepts	✅	✅
Research Papers	✅	✅
Project Planning	❌	✅
Timeline Estimation	❌	✅
Budget Guidance	❌	✅
Domain-Specific Advice	Limited	✅

📝 License

Apache 2.0

🔗 Links

• V1 Model: Xen0pp/Smollm3_720prms
• Base Model: HuggingFaceTB/SmolLM2-360M-Instruct

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Note: This is a fine-tuned small model for educational purposes. Always validate recommendations with domain expertise.