cstr/f2llm-v2-0.6b-gguf overview

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    "license": "mit",
    "language": [
      "multilingual"
    ],
    "tags": [
      "embeddings",
      "gguf",
      "ggml",
      "text-embeddings",
      "qwen3",
      "crispembed",
      "ollama"
    ],
    "pipeline_tag": "feature-extraction",
    "base_model": "codefuse-ai/F2LLM-0.6B",
    "frontmatter": {
      "license": "mit",
      "language": "[multilingual]",
      "tags": "[embeddings, gguf, ggml, text-embeddings, qwen3, crispembed, ollama]",
      "pipeline_tag": "feature-extraction",
      "base_model": "codefuse-ai/F2LLM-0.6B"
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    "hero_image_url": "",
    "summary": "GGUF format of codefuse-ai/F2LLM-0.6B for use with CrispEmbed and Ollama.",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nlicense: mit\nlanguage: [multilingual]\ntags: [embeddings, gguf, ggml, text-embeddings, qwen3, crispembed, ollama]\npipeline_tag: feature-extraction\nbase_model: codefuse-ai/F2LLM-0.6B\n---\n\n# f2llm-v2-0.6b GGUF\n\nGGUF format of [codefuse-ai/F2LLM-0.6B](https://huggingface.co/codefuse-ai/F2LLM-0.6B) for use with [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed) and [Ollama](https://ollama.com).\n\n## Files\n\n| File | Quantization | Size |\n|------|-------------|------|\n| [f2llm-v2-0.6b-q4_k.gguf](https://huggingface.co/cstr/f2llm-v2-0.6b-GGUF/resolve/main/f2llm-v2-0.6b-q4_k.gguf) | Q4_K | 0 MB |\n| [f2llm-v2-0.6b-q5_k.gguf](https://huggingface.co/cstr/f2llm-v2-0.6b-GGUF/resolve/main/f2llm-v2-0.6b-q5_k.gguf) | Q5_K | 0 MB |\n| [f2llm-v2-0.6b-q8_0.gguf](https://huggingface.co/cstr/f2llm-v2-0.6b-GGUF/resolve/main/f2llm-v2-0.6b-q8_0.gguf) | Q8_0 | 0 MB |\n| [f2llm-v2-0.6b.gguf](https://huggingface.co/cstr/f2llm-v2-0.6b-GGUF/resolve/main/f2llm-v2-0.6b.gguf) | F32 | 0 MB |\n\n**Recommended:** Q8_0 for quality (cos vs HF: L2=1.0), Q4_K for size (L2=1.0).\n\n## Quick Start\n\n### CrispEmbed\n```bash\n./crispembed -m f2llm-v2-0.6b \"Hello world\"\n./crispembed-server -m f2llm-v2-0.6b --port 8080\n```\n\n### Ollama (with [CrispStrobe fork](https://github.com/CrispStrobe/ollama/tree/feat/xlmr-embedding))\n```bash\necho \"FROM f2llm-v2-0.6b-q8_0.gguf\" > Modelfile\nollama create f2llm-v2-0.6b -f Modelfile\ncurl http://localhost:11434/api/embed -d '{\"model\":\"f2llm-v2-0.6b\",\"input\":[\"Hello world\"]}'\n```\n\n### Python (CrispEmbed)\n```python\nfrom crispembed import CrispEmbed\nmodel = CrispEmbed(\"f2llm-v2-0.6b-q8_0.gguf\")\nvectors = model.encode([\"Hello world\", \"Goodbye world\"])\n```\n\n## Model Details\n\n| Property | Value |\n|----------|-------|\n| Architecture | Qwen3 |\n| Parameters | 600M |\n| Embedding Dimension | 1024 |\n| Layers | 28 |\n| Pooling | last-token |\n| Tokenizer | BPE |\n| Language | multilingual |\n| Q8_0 vs HuggingFace | L2=1.0 |\n| Q4_K vs HuggingFace | L2=1.0 |\n\n## Server API\n\nCrispEmbed server supports four API dialects:\n- `POST /embed` -- native\n- `POST /v1/embeddings` -- OpenAI-compatible\n- `POST /api/embed` -- Ollama-compatible\n- `POST /api/embeddings` -- Ollama legacy\n\n## Credits\n\n- Original model: [codefuse-ai/F2LLM-0.6B](https://huggingface.co/codefuse-ai/F2LLM-0.6B)\n- Inference: [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed) (MIT, ggml-based)\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "embeddings",
    "ggml",
    "text-embeddings",
    "qwen3",
    "crispembed",
    "ollama",
    "feature-extraction",
    "multilingual",
    "base_model:codefuse-ai/F2LLM-0.6B",
    "base_model:quantized:codefuse-ai/F2LLM-0.6B",
    "license:mit",
    "endpoints_compatible",
    "region:us"
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  "last_modified": "2026-04-16T05:28:29.000Z",
  "created_at": "2026-04-15T03:31:45.000Z",
  "pipeline_tag": "feature-extraction",
  "library_name": ""
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