Support matrix#
Local embedded models#
The following HuggingFace model variants have been tested and verified
to work with AIDB’s local model providers. You can use any of these
identifiers with the corresponding config helper when calling
aidb.create_model() .
BERT#
Model name |
HuggingFace identifier |
Config string |
|---|---|---|
bert_all-MiniLM-L12-v1 |
sentence-transformers/all-MiniLM-L12-v1 |
aidb.bert_config('sentence-transformers/all-MiniLM-L12-v1', revision => 'main') |
bert_all-MiniLM-L6-v1 |
sentence-transformers/all-MiniLM-L6-v1 |
aidb.bert_config('sentence-transformers/all-MiniLM-L6-v1', revision => 'main') |
bert_all-MiniLM-L6-v2 |
sentence-transformers/all-MiniLM-L6-v2 |
aidb.bert_config('sentence-transformers/all-MiniLM-L6-v2', revision => 'main') |
bert_all-distilroberta-v1 |
sentence-transformers/all-distilroberta-v1 |
aidb.bert_config('sentence-transformers/all-distilroberta-v1', revision => 'main') |
bert_msmarco-bert-base-dot-v5 |
sentence-transformers/msmarco-bert-base-dot-v5 |
aidb.bert_config('sentence-transformers/msmarco-bert-base-dot-v5', revision => 'main') |
bert_multi-qa-MiniLM-L6-cos-v1 |
sentence-transformers/multi-qa-MiniLM-L6-cos-v1 |
aidb.bert_config('sentence-transformers/multi-qa-MiniLM-L6-cos-v1', revision => 'main') |
bert_multi-qa-MiniLM-L6-dot-v1 |
sentence-transformers/multi-qa-MiniLM-L6-dot-v1 |
aidb.bert_config('sentence-transformers/multi-qa-MiniLM-L6-dot-v1', revision => 'main') |
bert_paraphrase-TinyBERT-L6-v2 |
sentence-transformers/paraphrase-TinyBERT-L6-v2 |
aidb.bert_config('sentence-transformers/paraphrase-TinyBERT-L6-v2', revision => 'main') |
bert_paraphrase-multilingual-MiniLM-L12-v2 |
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
aidb.bert_config('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2', revision => 'main') |
bert_paraphrase-multilingual-mpnet-base-v2 |
sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
aidb.bert_config('sentence-transformers/paraphrase-multilingual-mpnet-base-v2', revision => 'main') |
All BERT variants support text embedding (aidb.encode_text() ).
CLIP#
Model name |
HuggingFace identifier |
Config string |
|---|---|---|
openai/clip-vit-base-patch32 |
openai/clip-vit-base-patch32 |
aidb.clip_config('openai/clip-vit-base-patch32') |
CLIP supports both text embedding (aidb.encode_text() ) and image
embedding (aidb.encode_image() ).
Llama#
Model name |
HuggingFace identifier |
Config string |
|---|---|---|
HuggingFaceTB/SmolLM2-1.7B-Instruct |
HuggingFaceTB/SmolLM2-1.7B-Instruct |
aidb.llama_config('HuggingFaceTB/SmolLM2-1.7B-Instruct', revision => 'main') |
HuggingFaceTB/SmolLM2-135M-Instruct |
HuggingFaceTB/SmolLM2-135M-Instruct |
aidb.llama_config('HuggingFaceTB/SmolLM2-135M-Instruct', revision => 'main') |
HuggingFaceTB/SmolLM2-360M-Instruct |
HuggingFaceTB/SmolLM2-360M-Instruct |
aidb.llama_config('HuggingFaceTB/SmolLM2-360M-Instruct', revision => 'main') |
TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
aidb.llama_config('TinyLlama/TinyLlama-1.1B-Chat-v1.0', revision => 'main') |
All Llama variants support text generation (aidb.decode_text() ).
T5#
Model name |
HuggingFace identifier |
Config string |
|---|---|---|
t5-small |
t5-small |
aidb.t5_config('t5-small', revision => 'main') |
t5-base |
t5-base |
aidb.t5_config('t5-base', revision => 'main') |
t5-large |
t5-large |
aidb.t5_config('t5-large', revision => 'main') |
T5 variants support both text encoding (aidb.encode_text() ) and
text generation (aidb.decode_text() ).
!!!Note These are the pre-vetted variants confirmed to work with AIDB. You are not limited to these — any compatible HuggingFace model can be used with the appropriate config helper, but only the variants listed here have been tested by EDB.
NIM models#
AIDB supports NVIDIA NIM microservices via several providers. The tables below list the compatible NIM models for each provider. Models marked as Default on HM are shipped with EDB Hybrid Manager’s Model Serving and are available out of the box in HM deployments — no separate deployment required.
NIM completions (nim_completions )#
Supports: aidb.decode_text() , aidb.decode_text_batch()
NIM model identifier |
Default on HM |
|---|---|
nvidia/nemotron-3-nano |
OK |
nvidia/llama-3.3-nemotron-super-49b-v1 |
OK |
ibm/granite-guardian-3.0-8b |
— |
ibm/granite-3.0-8b-instruct |
— |
ibm/granite-3.0-3b-a800m-instruct |
— |
meta/llama-3.3-70b-instruct |
— |
meta/llama-3.2-3b-instruct |
— |
meta/llama-3.2-1b-instruct |
— |
meta/llama-3.1-405b-instruct |
— |
meta/llama-3.1-70b-instruct |
— |
meta/llama-3.1-8b-instruct |
— |
meta/llama3-70b-instruct |
— |
meta/llama3-8b-instruct |
— |
nvidia/llama-3.1-nemotron-70b-instruct |
— |
nvidia/llama-3.1-nemotron-51b-instruct |
— |
nvidia/nemotron-mini-4b-instruct |
— |
nvidia/nemotron-4-340b-instruct |
— |
google/shieldgemma-9b |
— |
google/gemma-7b |
— |
google/codegemma-7b |
— |
NIM embeddings (nim_embeddings )#
Supports: aidb.encode_text() , aidb.encode_text_batch()
NIM model identifier |
Default on HM |
|---|---|
nvidia/llama-3.2-nemoretriever-300m-embed-v1 |
OK |
nvidia/nv-embedqa-e5-v5 |
— |
NIM CLIP (nim_clip )#
Supports: aidb.encode_text() , aidb.encode_text_batch() ,
aidb.encode_image()
NIM model identifier |
Default on HM |
|---|---|
nvidia/nvclip |
OK |
NIM OCR (nim_paddle_ocr )#
Supports: aidb.perform_ocr()
NIM model identifier |
Default on HM |
|---|---|
baidu/paddleocr |
OK |
NIM reranking (nim_reranking )#
Supports: aidb.rerank_text()
NIM model identifier |
Default on HM |
|---|---|
nvidia/llama-3.2-nv-rerankqa-1b-v2 |
OK |
!!!Note Models marked as Default on HM are pre-deployed by EDB
Hybrid Manager’s Model Serving. All other models can be self-hosted
using KServe and registered with AIDB using aidb.create_model() .
For setup instructions, see NVIDIA NIM .