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Model: jamesjk4242/insighta-mandala-v13
Source: Original Platform

README.md

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+---
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - qwen3
+  - gguf
+  - mandala
+  - education
+  - fine-tuned
+language:
+  - ko
+  - en
+license: apache-2.0
+---
+
+# Insighta Mandala v13
+
+Fine-tuned Qwen3-4B model for generating mandala learning plans in JSON format.
+
+## Model Details
+
+- **Base model**: Qwen/Qwen3-4B
+- **Fine-tuning**: LoRA on mandala learning plan generation task
+- **Languages**: Korean, English
+- **Output format**: Structured JSON (mandala chart format)
+
+## Available Quantizations
+
+| Format | Size | Description |
+|--------|------|-------------|
+| `model.safetensors` | ~8GB | Full F16 weights |
+| `insighta-mandala-v13-Q8_0.gguf` | ~4GB | Q8_0 quantized GGUF |
+| `insighta-mandala-v13-Q4_K_M.gguf` | ~2.4GB | Q4_K_M quantized GGUF (recommended for CPU) |
+
+## Usage
+
+### With llama-cpp-python
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(model_path="insighta-mandala-v13-Q4_K_M.gguf", n_ctx=4096)
+output = llm(
+    "<|im_start|>user\nTOEFL 100점 만다라트 차트를 만들어줘<|im_end|>\n<|im_start|>assistant\n",
+    max_tokens=2048,
+    temperature=0.7,
+)
+print(output["choices"][0]["text"])
+```
+
+### With HF Space API
+
+```bash
+curl -X POST https://jamesjk4242-insighta-mandala-v13-api.hf.space/api/predict \
+  -H "Content-Type: application/json" \
+  -d '{"data": ["TOEFL 100점 만다라트 차트를 만들어줘", "You are a helpful assistant that generates mandala learning plans in JSON format.", 2048, 0.7, 0.9, true]}'
+```