SmolLM-ML-Planner-500-V3

🤖 SmolLM ML Project Planner V3 (500 Examples)

Production-grade ML project planning assistant - Your expert advisor for designing, scoping, and executing machine learning projects.

🌟 What's New in V3

Major Upgrade: Trained on 500 comprehensive ML project planning examples

Training Results (Excellent!)

• Final Training Loss: 0.0516 ⭐ (extremely low!)
• Validation Loss: 0.0516
• Training Examples: 450
• Validation Examples: 50
• Epochs: 5
• Training Time: ~6 minutes (Unsloth)
• GPU Memory: 1.076 GB peak

Capabilities

✅ Expert-level project initiation & scoping ✅ Comprehensive data strategy planning
✅ Domain-specific model selection (CV, NLP, RecSys, TS) ✅ Detailed & realistic timeline estimation ✅ Complete budget breakdowns ✅ Production-ready risk assessment ✅ Full MLOps & deployment guidance

🚀 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "Xen0pp/SmolLM-ML-Planner-500-V3",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Xen0pp/SmolLM-ML-Planner-500-V3")

# Ask for ML project planning advice
messages = [
    {"role": "system", "content": "You are an expert ML project planning advisor."},
    {"role": "user", "content": "How do I plan a recommendation system project?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=400, temperature=0.7, top_p=0.9)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

💡 Example Outputs

Input: "I have 300 labeled examples. Can I build a production model?"

V3 Output: Complete strategy including transfer learning, data augmentation, active learning, expected accuracy ranges, and cost-benefit analysis.

Input: "What's a realistic timeline for NLP sentiment analysis?"

V3 Output: Phase-by-phase breakdown: PoC (3-4 weeks, $10K-$25K), MVP (8-12 weeks, $50K-$100K), Production (5-7 months, $150K-$300K).

📊 Model Details

• Base: SmolLM2-360M-Instruct
• Parameters: 370M total, 8.7M trainable (2.34%)
• Context: 2048 tokens
• Size: 720MB (BF16)
• Training: Unsloth LoRA (r=16, alpha=16)

🆚 Version History

📝 License

Apache 2.0

🔗 Links

• Base Model: HuggingFaceTB/SmolLM2-360M-Instruct
• Training: Unsloth

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Built with ❤️ for the ML community - Helping practitioners plan successful ML projects