llama-nemoretriever-colembed-3b-v1

578
70
3.0B
llama_nemoretrievercolembed
by
nvidia
Image Model
OTHER
3B params
New
578 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
7GB+ RAM
Mobile
Laptop
Server
Quick Summary

The nvidia/llama-nemoretriever-colembed-3b-v1 is a late interaction embedding model fine-tuned for query-document retrieval.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
3GB+ RAM

Training Data Analysis

🟡 Average (4.8/10)

Researched training datasets used by llama-nemoretriever-colembed-3b-v1 with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (4)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering
wikipedia
🟡 5/10
science
multilingual
Key Strengths
  • •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
  • •Scientific Authority: Peer-reviewed content from established repository
  • •Domain-Specific: Specialized vocabulary and concepts
  • •Mathematical Content: Includes complex equations and notation
Considerations
  • •Specialized: Primarily technical and mathematical content
  • •English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Evaluates with Vidore V1 and V2bash
pip install "mteb>=2.3.10,<3.0.0"
# Evaluates with Vidore V1 and V2
CUDA_VISIBLE_DEVICES=0; python3 mteb2_eval.py --model_name nvidia/llama-nemoretriever-colembed-3b-v1 --batch_size 16 --benchmark "VisualDocumentRetrieval"
# Evaluates with Vidore V3
CUDA_VISIBLE_DEVICES=0; python3 mteb2_eval.py --model_name nvidia/llama-nemoretriever-colembed-3b-v1 --batch_size 16 --benchmark "ViDoRe(v3)"
# Evaluates with a specific task/dataset of Vidore V3: Vidore3ComputerScienceRetrieval
CUDA_VISIBLE_DEVICES=0; python3 mteb2_eval.py --model_name nvidia/llama-nemoretriever-colembed-3b-v1 --batch_size 16 --benchmark "ViDoRe(v3)" --task-list Vidore3ComputerScienceRetrieval
Downgrade transformers as vidore will install latest transformersbash
pip install git+https://github.com/illuin-tech/vidore-benchmark@e0eb9032e7e00adc8aa6f9cb35d5a9371f67485a
# Downgrade transformers as vidore will install latest transformers
pip install --upgrade transformers==4.49.0
CUDA_VISIBLE_DEVICES=0; python3 vidore_eval.py --model_name_or_path nvidia/llama-nemoretriever-colembed-3b-v1 --savedir_datasets ./results/ --model_revision 50c36f4d5271c6851aa08bd26d69f6e7ca8b870c

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