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memoryco-ai/llama-nemotron-rerank-1b-v2-GGUF
llama-nemotron-rerank-1b-v2-GGUF is a machine learning model from memoryco-ai. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
GGUF quantizations of nvidia/llama-nemotron-rerank-1b-v2 for use with llama.cpp.
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From the Hugging Face model README
GGUF quantizations of nvidia/llama-nemotron-rerank-1b-v2 for use with llama.cpp.
These GGUFs require a patched llama.cpp that supports reranking via the llama-embed architecture. Specifically:
LLM_TENSOR_CLS and LLM_TENSOR_CLS_OUT in the LLM_ARCH_LLAMA_EMBED tensor allowlistcausal_attn and pooling_type hparam reads in the LLM_ARCH_LLAMA_EMBED caseThe memoryco/llama.cpp fork (branch pinned-for-rs) includes both patches.
The initial convert_hf_to_gguf.py conversion produced a GGUF that wouldn't load correctly for reranking. These post-conversion fixes were applied:
cls.weight → cls.output.weight (maps to LLM_TENSOR_CLS_OUT prefix "cls.output")[2048] (1D) → {2048, 1} (2D classification head)llama-embed.classifier.output_labels: ["relevance"] (sets n_cls_out=1)tokenizer.ggml.add_eos_token: true (required for non-zero rerank scores)| File | Quant | Size |
|---|---|---|
| llama-nemotron-rerank-1b-v2.Q8_0.gguf | Q8_0 | ~1.25 GB |
| llama-nemotron-rerank-1b-v2.Q4_K_M.gguf | Q4_K_M | ~770 MB |
# Using the patched memoryco/llama.cpp fork
./build/bin/llama-embedding \
-m llama-nemotron-rerank-1b-v2.Q8_0.gguf \
--pooling rank \
-p 'passage: Paris is the capital of France. query: what is the capital of france'
Input format: passage: {document_text}. query: {query_text} as a single string. The model outputs a single float score (higher = more relevant).
Converted by memoryco-ai using llama.cpp's convert_hf_to_gguf.py with custom architecture support and post-conversion GGUF fixes.