---
language:
- en
license: apache-2.0
tags:
- smollm
- fine-tuned
- machine-learning
- ml-project-planning
- ml-advisor
- unsloth
library_name: transformers
base_model: Xen0pp/Smollm3_720prms
metrics:
- loss
pipeline_tag: text-generation
widget:
- text: "How do I plan a customer churn prediction project?"
  example_title: "Project Planning"
- text: "I have only 500 labeled examples. What's my strategy?"
  example_title: "Small Data Strategy"
- text: "What's a realistic timeline for an NLP project?"
  example_title: "Timeline Estimation"
---

# 🤖 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
```python
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

| Version | Examples | Loss | Use Case |
|---------|----------|------|----------|
| V1 | 6 | - | Demo |
| V3 | 500 | 0.0516 | **Production** ⭐ |

## 📝 License

Apache 2.0

## 🔗 Links

- **Base Model:** [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct)
- **Training:** [Unsloth](https://github.com/unslothai/unsloth)

---

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