granite-embedding-reranker-english-r2-GGUF
GGUF F16 conversion of ibm-granite/granite-embedding-reranker-english-r2.
Model Summary
This repository provides an F16 GGUF export of the original IBM Granite English reranker / embedding model for use in GGUF-compatible tooling where supported.
- Base model:
ibm-granite/granite-embedding-reranker-english-r2 - Format: GGUF
- Precision: F16
- Quantization: None
- Source: Hugging Face original model repo by IBM Granite
This repository is a format conversion of the original model.
Please refer to the original model card for authoritative details on training data, intended uses, limitations, and evaluation.
Files
granite-embedding-reranker-english-r2-f16.gguf— GGUF model in full F16 precision
Provenance
- Original model:
ibm-granite/granite-embedding-reranker-english-r2 - Converted by:
YOUR_USERNAME - GGUF conversion tool:
llama.cpp/convert_hf_to_gguf.py - Conversion type: HF → GGUF
- Output precision: F16
Intended Use
This model is intended for embedding / reranking tasks, subject to support in the target runtime.
Typical use cases may include:
- semantic ranking
- retrieval re-ranking
- passage-query scoring
- information retrieval pipelines
- search relevance improvement
Important Compatibility Note
This is not a generative chat model. It is a converted reranker / embedding-style model.
GGUF is most commonly used with runtimes centered around LLM inference.
Support for encoder-style, embedding, or reranker architectures may vary depending on the runtime and version.
Before using this file, verify that your target GGUF runtime supports:
- the underlying model architecture
- the required pooling / scoring behavior
- encoder or reranker inference modes if applicable
Example Download
Using huggingface-cli:
huggingface-cli download YOUR_USERNAME/granite-embedding-reranker-english-r2-GGUF \
granite-embedding-reranker-english-r2-f16.gguf \
--local-dir .