Llama-xLAM-2-8b-fc-r-gguf
8.4K
14
8.0B
1 language
Q4
llama
by
Salesforce
Language Model
OTHER
8B params
New
8K downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary
This repo provides the GGUF format for the Llama-xLAM-2-8b-fc-r model.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
8GB+ RAM
Training Data Analysis
š” Average (4.8/10)
Researched training datasets used by Llama-xLAM-2-8b-fc-r-gguf with quality assessment
Specialized For
general
science
multilingual
reasoning
Training Datasets (4)
common crawl
š“ 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
šµ 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
š” 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
š” 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 DatasetsCode Examples
How to Download GGUF Filesbash
pip install huggingface-hubHow to Download GGUF Filesbash
huggingface-cli loginPython Frameworkbash
pip install llama-cpp-pythonPython Frameworkpythonllama.cpp
from llama_cpp import Llama
llm = Llama(
model_path="[PATH-TO-MODEL]"
)
output = llm.create_chat_completion(
messages = [
{
"role": "system",
"content": "You are a helpful assistant that can use tools. You are developed by Salesforce xLAM team."
},
{
"role": "user",
"content": "Extract Jason is 25 years old"
}
],
tools=[{
"type": "function",
"function": {
"name": "UserDetail",
"parameters": {
"type": "object",
"title": "UserDetail",
"properties": {
"name": {
"title": "Name",
"type": "string"
},
"age": {
"title": "Age",
"type": "integer"
}
},
"required": [ "name", "age" ]
}
}
}],
tool_choice={
"type": "function",
"function": {
"name": "UserDetail"
}
}
)
print(output['choices'][0]['message'])Deploy This Model
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