rim89987/deepseek-r1-0528-qwen3-8b-abliterated-q5_0-gguf overview

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    "license": "mit",
    "language": [
      "en",
      "bn"
    ],
    "base_model": [
      "deepseek-ai/DeepSeek-R1"
    ],
    "frontmatter": {
      "license": "mit",
      "language": [
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        "bn"
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      "base_model": [
        "deepseek-ai/DeepSeek-R1"
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    "summary": "Model name: DeepSeek-R1-0528-Qwen3-8B-abliterated-Q5_0-GGUF Format: GGUF Quantization: Q5_0 Base model: DeepSeek-R1-0528 / Qwen3-8B Converted by: rim89987 📌 Description This is a GGUF-converted and quantized version of the DeepSeek-R1-0528 Qwen3-8B abliterated model. The model is optimized for local and lightweight inference using llama.cpp and compatible runtimes. The Q5_0 quantization offers a strong balance between performance, speed, and memory efficiency, making it suitable for systems with limited VRAM or free cloud environments (e.g., Colab, Kaggle). 🚀 Use Cases General chat & assistant tasks Reasoning and logical responses Coding assistance (light to medium) Local LLM experiments GGUF-based inference pipelines ⚙️ Compatibility llama.cpp llama-cpp-python Ollama (GGUF support) Text-generation-webui (llama backend) 💾 System Requirements (Approx.) RAM / VRAM: ~6–7 GB Recommended for CPU or low-end GPU setups 🧾 Credits Original model: DeepSeek / Qwen GGUF conversion & quantization: rim89987",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nlicense: mit\nlanguage:\n- en\n- bn\nbase_model:\n- deepseek-ai/DeepSeek-R1\n---\nModel name:\nDeepSeek-R1-0528-Qwen3-8B-abliterated-Q5_0-GGUF\n\nFormat: GGUF\nQuantization: Q5_0\nBase model: DeepSeek-R1-0528 / Qwen3-8B\nConverted by: rim89987\n\n📌 Description\n\nThis is a GGUF-converted and quantized version of the DeepSeek-R1-0528 Qwen3-8B abliterated model.\nThe model is optimized for local and lightweight inference using llama.cpp and compatible runtimes.\n\nThe Q5_0 quantization offers a strong balance between performance, speed, and memory efficiency, making it suitable for systems with limited VRAM or free cloud environments (e.g., Colab, Kaggle).\n\n🚀 Use Cases\n\nGeneral chat & assistant tasks\n\nReasoning and logical responses\n\nCoding assistance (light to medium)\n\nLocal LLM experiments\n\nGGUF-based inference pipelines\n\n⚙️ Compatibility\n\nllama.cpp\n\nllama-cpp-python\n\nOllama (GGUF support)\n\nText-generation-webui (llama backend)\n\n💾 System Requirements (Approx.)\n\nRAM / VRAM: ~6–7 GB\n\nRecommended for CPU or low-end GPU setups\n\n🧾 Credits\n\nOriginal model: DeepSeek / Qwen\n\nGGUF conversion & quantization: rim89987",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "en",
    "bn",
    "base_model:deepseek-ai/DeepSeek-R1",
    "base_model:quantized:deepseek-ai/DeepSeek-R1",
    "license:mit",
    "endpoints_compatible",
    "region:us",
    "conversational"
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  "last_modified": "2026-01-10T12:00:10.000Z",
  "created_at": "2026-01-10T11:54:26.000Z",
  "pipeline_tag": "",
  "library_name": ""
}

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