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README.md

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+---
+license: mit
+tags:
+  - clip
+  - text
+  - gguf
+  - crispembed
+  - text-embedding
+pipeline_tag: feature-extraction
+library_name: ggml
+---
+
+# CLIP Text Encoder Base (GGUF)
+
+GGUF conversion of the CLIP text encoder (base) for use with [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed). Extracted from [openai/clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16).
+
+- **Architecture:** CLIP text transformer with causal attention
+- **Parameters:** 63M
+- **Output:** 512-dimensional L2-normalized embeddings
+- **Tokenizer:** BPE tokenizer (embedded in GGUF), max 77 tokens
+- **Size:** ~244 MB
+
+## Usage
+
+```bash
+# Embed a single text
+crispembed -m clip-text-base "a photo of a cat"
+
+# Embed from file
+crispembed -m clip-text-base --input queries.txt --output embeddings.bin
+```
+
+## Cross-modal pairing
+
+Output embeddings live in the same vector space as [cstr/clip-vit-base-patch16-GGUF](https://huggingface.co/cstr/clip-vit-base-patch16-GGUF). Use both for zero-shot image-text retrieval:
+
+```bash
+crispembed -m clip-text-base "a photo of a cat"               # text embedding
+crispembed -m clip-vit-base-patch16 --image photo.jpg          # vision embedding
+# cosine similarity measures image-text alignment
+```
+
+## Notes
+
+- All output embeddings are L2-normalized.
+- BPE tokenizer is bundled inside the GGUF file; no external vocab files needed.
+- This is a GGUF conversion; weights are numerically equivalent to the original HuggingFace model.