116 lines
3.4 KiB
Markdown
116 lines
3.4 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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tags:
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- smollm
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- fine-tuned
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- machine-learning
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- ml-project-planning
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- ml-advisor
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- unsloth
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library_name: transformers
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base_model: Xen0pp/Smollm3_720prms
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metrics:
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- loss
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pipeline_tag: text-generation
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widget:
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- text: "How do I plan a customer churn prediction project?"
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example_title: "Project Planning"
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- text: "I have only 500 labeled examples. What's my strategy?"
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example_title: "Small Data Strategy"
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- text: "What's a realistic timeline for an NLP project?"
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example_title: "Timeline Estimation"
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---
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# 🤖 SmolLM ML Project Planner V3 (500 Examples)
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**Production-grade ML project planning assistant** - Your expert advisor for designing, scoping, and executing machine learning projects.
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## 🌟 What's New in V3
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**Major Upgrade:** Trained on **500 comprehensive ML project planning examples**
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### Training Results (Excellent!)
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- **Final Training Loss:** 0.0516 ⭐ (extremely low!)
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- **Validation Loss:** 0.0516
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- **Training Examples:** 450
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- **Validation Examples:** 50
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- **Epochs:** 5
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- **Training Time:** ~6 minutes (Unsloth)
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- **GPU Memory:** 1.076 GB peak
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### Capabilities
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✅ Expert-level project initiation & scoping
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✅ Comprehensive data strategy planning
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✅ Domain-specific model selection (CV, NLP, RecSys, TS)
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✅ Detailed & realistic timeline estimation
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✅ Complete budget breakdowns
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✅ Production-ready risk assessment
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✅ Full MLOps & deployment guidance
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## 🚀 Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Load model
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model = AutoModelForCausalLM.from_pretrained(
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"Xen0pp/SmolLM-ML-Planner-500-V3",
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("Xen0pp/SmolLM-ML-Planner-500-V3")
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# Ask for ML project planning advice
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messages = [
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{"role": "system", "content": "You are an expert ML project planning advisor."},
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{"role": "user", "content": "How do I plan a recommendation system project?"}
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]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
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outputs = model.generate(inputs, max_new_tokens=400, temperature=0.7, top_p=0.9)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## 💡 Example Outputs
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**Input:** "I have 300 labeled examples. Can I build a production model?"
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**V3 Output:** Complete strategy including transfer learning, data augmentation, active learning, expected accuracy ranges, and cost-benefit analysis.
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**Input:** "What's a realistic timeline for NLP sentiment analysis?"
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**V3 Output:** Phase-by-phase breakdown: PoC (3-4 weeks, $10K-$25K), MVP (8-12 weeks, $50K-$100K), Production (5-7 months, $150K-$300K).
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## 📊 Model Details
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- **Base:** SmolLM2-360M-Instruct
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- **Parameters:** 370M total, 8.7M trainable (2.34%)
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- **Context:** 2048 tokens
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- **Size:** 720MB (BF16)
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- **Training:** Unsloth LoRA (r=16, alpha=16)
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## 🆚 Version History
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| Version | Examples | Loss | Use Case |
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|---------|----------|------|----------|
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| V1 | 6 | - | Demo |
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| V3 | 500 | 0.0516 | **Production** ⭐ |
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## 📝 License
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Apache 2.0
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## 🔗 Links
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- **Base Model:** [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct)
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- **Training:** [Unsloth](https://github.com/unslothai/unsloth)
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---
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*Built with ❤️ for the ML community - Helping practitioners plan successful ML projects*
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