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README.md
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base_model: unsloth/Qwen3-0.6B-unsloth-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen3
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- gguf
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license: apache-2.0
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language:
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- en
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datasets:
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- srisree/nextjs_typescript_fim_dataset
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---
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- ✅ Training configuration (LoRA, batch size, sequence length, etc.)
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- ✅ Evaluation and inference examples
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🚀 **Coming soon...** Stay tuned for the full release!
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- ✅ Training configuration (LoRA, batch size, sequence length, etc.)
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- ✅ Evaluation and inference examples
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🚀 **Coming soon...** Stay tuned for the full release!
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---
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# ⚙️ Setup and Run NanoCoder Locally with Ollama in VS Code
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> Step-by-step guide to install, configure, and use **NanoCoder** for intelligent React frontend code completion with the **Continue** VS Code extension.
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---
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## 🧠 Prerequisites
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Before getting started, ensure you have the following installed:
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- [VS Code](https://code.visualstudio.com/)
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- [Ollama](https://ollama.ai) (latest version)
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- [Continue extension](https://marketplace.visualstudio.com/items?itemName=Continue.continue)
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- A system with at least **8GB RAM** (recommended for 0.6B models)
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---
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## 🧩 Step 1: Install Ollama
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If you haven’t already, download and install **Ollama**:
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- macOS / Linux / Windows: [https://ollama.ai/download](https://ollama.ai/download)
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Once installed, open your terminal and verify the installation.
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## 💾 Step 2: Pull NanoCoder Model
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>ollama pull srisree/nanocoder
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## ⚡ Step 3: Run NanoCoder with Ollama
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Once downloaded, you can test NanoCoder directly in the terminal:
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>ollama run nanocoder
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Read more [Continue Docs](https://docs.continue.dev/customize/deep-dives/autocomplete)
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