Llama-xLAM-2-8b-fc-r

4.7K
54
8.0B
1 language
llama
by
Salesforce
Language Model
OTHER
8B params
New
5K downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary

Large Action Models (LAMs) are advanced language models designed to enhance decision-making by translating user intentions into executable actions.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
8GB+ RAM

Training Data Analysis

šŸŸ” Average (4.8/10)

Researched training datasets used by Llama-xLAM-2-8b-fc-r with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (4)

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

Explore our comprehensive training dataset analysis

View All Datasets

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.