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* add cdn image links * changeset --------- Co-authored-by: Elephant Lumps <celestial_vault@Elephants-MacBook-Pro.local>
110 lines
4.9 KiB
Plaintext
110 lines
4.9 KiB
Plaintext
---
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title: "Read Me First"
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---
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## Running Local Models with Cline: What You Need to Know 🤖
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Cline is a powerful AI coding assistant that uses tool-calling to help you write, analyze, and modify code. While running models locally can save on API costs, there's an important trade-off: local models are significantly less reliable at using these essential tools.
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## Why Local Models Are Different 🔬
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When you run a "local version" of a model, you're actually running a drastically simplified copy of the original. This process, called distillation, is like trying to compress a professional chef's knowledge into a basic cookbook – you keep the simple recipes but lose the complex techniques and intuition.
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Local models are created by training a smaller model to imitate a larger one, but they typically only retain 1-26% of the original model's capacity. This massive reduction means:
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- Less ability to understand complex contexts
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- Reduced capability for multi-step reasoning
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- Limited tool-use abilities
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- Simplified decision-making process
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Think of it like running your development environment on a calculator instead of a computer – it might handle basic tasks, but complex operations become unreliable or impossible.
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<Frame>
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<img
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src="https://storage.googleapis.com/cline_public_images/docs/assets/image%20(4).png"
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alt="Local model comparison diagram"
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/>
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</Frame>
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### What Actually Happens
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When you run a local model with Cline:
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#### Performance Impact 📉
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- Responses are 5-10x slower than cloud services
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- System resources (CPU, GPU, RAM) get heavily utilized
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- Your computer may become less responsive for other tasks
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#### Tool Reliability Issues 🛠️
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- Code analysis becomes less accurate
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- File operations may be unreliable
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- Browser automation capabilities are reduced
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- Terminal commands might fail more often
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- Complex multi-step tasks often break down
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### Hardware Requirements 💻
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You'll need at minimum:
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- Modern GPU with 8GB+ VRAM (RTX 3070 or better)
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- 32GB+ system RAM
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- Fast SSD storage
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- Good cooling solution
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Even with this hardware, you'll be running smaller, less capable versions of models:
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| Model Size | What You Get |
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| ---------- | ------------------------------------------------------- |
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| 7B models | Basic coding, limited tool use |
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| 14B models | Better coding, unstable tool use |
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| 32B models | Good coding, inconsistent tool use |
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| 70B models | Best local performance, but requires expensive hardware |
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Put simply, the cloud (API) versions of these models are the full-bore version of the model. The full version of DeepSeek-R1 is 671B. These distilled models are essentially "watered-down" versions of the cloud model.
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### Practical Recommendations 💡
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#### Consider This Approach
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1. Use cloud models for:
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- Complex development tasks
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- When tool reliability is crucial
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- Multi-step operations
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- Critical code changes
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2. Use local models for:
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- Simple code completion
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- Basic documentation
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- When privacy is paramount
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- Learning and experimentation
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#### If You Must Go Local
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- Start with smaller models
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- Keep tasks simple and focused
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- Save work frequently
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- Be prepared to switch to cloud models for complex operations
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- Monitor system resources
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### Common Issues 🚨
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- **"Tool execution failed":** Local models often struggle with complex tool chains. Simplify your prompt.
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- **"No connection could be made because the target machine actively refused it":** This usually means that the Ollama or LM Studio server isn't running, or is running on a different port/address than Cline is configured to use. Double-check the Base URL address in your API Provider settings.
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- **"Cline is having trouble...":** Increase your model's context length to its maximum size.
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- **Slow or incomplete responses:** Local models can be slower than cloud-based models, especially on less powerful hardware. If performance is an issue, try using a smaller model. Expect significantly longer processing times.
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- **System stability:** Watch for high GPU/CPU usage and temperature
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- **Context limitations:** Local models often have smaller context windows than cloud models. Break tasks down into smaller pieces.
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### Looking Ahead 🔮
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Local model capabilities are improving, but they're not yet a complete replacement for cloud services, especially for Cline's tool-based functionality. Consider your specific needs and hardware capabilities carefully before committing to a local-only approach.
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### Need Help? 🤝
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- Join our [Discord](https://discord.gg/cline) community and [r/cline](https://www.reddit.com/r/CLine/)
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- Check the latest compatibility guides
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- Share your experiences with other developers
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Remember: When in doubt, prioritize reliability over cost savings for important development work.
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