初始化项目，由ModelHub XC社区提供模型

Model: Xen0pp/SmolLM-ML-Planner-500-V3
Source: Original Platform

README.md

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+---
+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*