Llama-Opus-Z8

17
llama
by
Daemontatox
Language Model
OTHER
New
17 downloads
Early-stage
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Edge AI:
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Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🟡 Average (4.8/10)

Researched training datasets used by Llama-Opus-Z8 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

SGLangbash
# Install SGLang
pip install "sglang[all]"

# Launch server
python -m sglang.launch_server \
    --model-path Daemontatox/Llama-Opus-Z8 \
    --dtype bfloat16 \
    --port 30000 \
    --context-length 8192

# Python client
import sglang as sgl

@sgl.function
def reasoning_task(s, question):
    s += sgl.system("You are a helpful AI assistant specialized in reasoning.")
    s += sgl.user(question)
    s += sgl.assistant(sgl.gen("answer", max_tokens=512, temperature=0.7))

# Initialize runtime
runtime = sgl.Runtime(
    model_path="Daemontatox/Llama-Opus-Z8",
    base_url="http://localhost:30000"
)
sgl.set_default_backend(runtime)

# Generate
state = reasoning_task.run(
    question="Solve: If x + 5 = 12, what is x?"
)
print(state["answer"])

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