Text Ranking
Transformers
Safetensors
multilingual
t5gemma2
text2text-generation
reranker
encoder-decoder
FBNL
Retrieval
RAG
Instructions to use KaLM-Embedding/KaLM-Reranker-V1-Nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KaLM-Embedding/KaLM-Reranker-V1-Nano with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("KaLM-Embedding/KaLM-Reranker-V1-Nano") model = AutoModelForMultimodalLM.from_pretrained("KaLM-Embedding/KaLM-Reranker-V1-Nano") - Notebooks
- Google Colab
- Kaggle
| set -euo pipefail | |
| export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0}" | |
| export VLLM_PLUGINS="${VLLM_PLUGINS:-kalm_t5gemma2}" | |
| if [[ -d /lib/x86_64-linux-gnu && -d /usr/lib/x86_64-linux-gnu ]]; then | |
| export LD_LIBRARY_PATH="/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" | |
| fi | |
| exec kalm-vllm-serve \ | |
| --host "${HOST:-0.0.0.0}" \ | |
| --port "${PORT:-8000}" \ | |
| --model "${MODEL:-KaLM-Embedding/KaLM-Reranker-V1-Nano}" \ | |
| --query-max-length "${QUERY_MAX_LENGTH:-512}" \ | |
| --document-max-length "${DOCUMENT_MAX_LENGTH:-1024}" \ | |
| --encoder-chunk-size "${ENCODER_CHUNK_SIZE:-4}" \ | |
| --max-model-len "${MAX_MODEL_LEN:-2048}" \ | |
| --batch-size "${BATCH_SIZE:-32}" \ | |
| --dtype "${DTYPE:-bfloat16}" \ | |
| --gpu-memory-utilization "${GPU_MEMORY_UTILIZATION:-0.85}" | |