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