You can deploy this model and start earning money today!

cstr/
all-MiniLM-L6-v2-GGUF

Name: all-MiniLM-L6-v2-GGUF
Availability: InStock

sentence-transformers/all-MiniLM-L6-v2

quantized

apache-2.0

Views

No views yet

all-MiniLM-L6-v2 GGUF

GGUF format of sentence-transformers/all-MiniLM-L6-v2 for use with CrispEmbed and Ollama.

Files

File	Quantization	Size
all-MiniLM-L6-v2-f32.gguf	F32	0 MB
all-MiniLM-L6-v2-q4_k.gguf	Q4_K	0 MB
all-MiniLM-L6-v2-q8_0.gguf	Q8_0	0 MB
all-MiniLM-L6-v2.gguf	F32	0 MB

Recommended: Q8_0 for quality (cos vs HF: 0.9998), Q4_K for size (0.970).

Quick Start

CrispEmbed

bash

./crispembed -m all-MiniLM-L6-v2 "Hello world"
./crispembed-server -m all-MiniLM-L6-v2 --port 8080

Ollama (with CrispStrobe fork)

bash

# Create model
echo "FROM all-MiniLM-L6-v2-q8_0.gguf" > Modelfile
ollama create all-MiniLM-L6-v2 -f Modelfile

# Embed
curl http://localhost:11434/api/embed -d '{"model":"all-MiniLM-L6-v2","input":["Hello world"]}'

Python (CrispEmbed)

python

from crispembed import CrispEmbed
model = CrispEmbed("all-MiniLM-L6-v2-q8_0.gguf")
vectors = model.encode(["Hello world", "Goodbye world"])

Model Details

Property	Value
Architecture	BERT
Parameters	22M
Embedding Dimension	384
Layers	6
Pooling	mean
Tokenizer	WordPiece
Language	en
Q8_0 vs HuggingFace	0.9998
Q4_K vs HuggingFace	0.970

Server API

CrispEmbed server supports four API dialects:

POST /embed — native
POST /v1/embeddings — OpenAI-compatible
POST /api/embed — Ollama-compatible
POST /api/embeddings — Ollama legacy

Credits

Original model: sentence-transformers/all-MiniLM-L6-v2
Inference: CrispEmbed (MIT, ggml-based)

Provenance and EU AI Act Art. 53 note

Upstream model: sentence-transformers/all-MiniLM-L6-v2 — published by sentence-transformers.
Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.

cstr/all-MiniLM-L6-v2-GGUF