cstr/clip-text-base-GGUF

CLIP Text Encoder Base (GGUF)

GGUF conversion of the CLIP text encoder (base) for use with CrispEmbed. Extracted from 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

# 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. Use both for zero-shot image-text retrieval:

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.