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

🟔 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
šŸ”“ 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
šŸ”µ 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

How to Download GGUF Filesbash
pip install huggingface-hub
How to Download GGUF Filesbash
huggingface-cli login
Python Frameworkbash
pip install llama-cpp-python
Python 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'])

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