DeepSeek-R1-Distill-Llama-70B-gguf
119
70.0B
2 languages
Q4
llama
by
mmnga
Language Model
OTHER
70B params
New
119 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
157GB+ RAM
Mobile
Laptop
Server
Quick Summary
AI model with specialized capabilities.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
66GB+ RAM
Training Data Analysis
đĄ Average (4.8/10)
Researched training datasets used by DeepSeek-R1-Distill-Llama-70B-gguf with quality assessment
Specialized For
general
science
multilingual
reasoning
Training Datasets (4)
common crawl
đŽ 2.5/10
general
science
Key Strengths
- âąScale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
- âąDiversity: The dataset captures billions of web pages across multiple domains and content types, ena...
- âąComprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
- âąBiased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
- âąLarge-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
c4
đ” 6/10
general
multilingual
Key Strengths
- âąScale and Accessibility: 750GB of publicly available, filtered text
- âąSystematic Filtering: Documented heuristics enable reproducibility
- âąLanguage Diversity: Despite English-only, captures diverse writing styles
Considerations
- âąEnglish-Only: Limits multilingual applications
- âąFiltering Limitations: Offensive content and low-quality text remain despite filtering
wikipedia
đĄ 5/10
science
multilingual
Key Strengths
- âąHigh-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
- âąMultilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
- âąStructured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
- âąLanguage Inequality: Low-resource language editions have significantly lower quality, fewer articles...
- âąBiased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
đĄ 5.5/10
science
reasoning
Key Strengths
- âąScientific Authority: Peer-reviewed content from established repository
- âąDomain-Specific: Specialized vocabulary and concepts
- âąMathematical Content: Includes complex equations and notation
Considerations
- âąSpecialized: Primarily technical and mathematical content
- âąEnglish-Heavy: Predominantly English-language papers
Explore our comprehensive training dataset analysis
View All DatasetsCode Examples
Usagetextllama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
build/bin/llama-cli -m 'DeepSeek-R1-Distill-Llama-70B-gguf' -n 128 -c 128 -p 'ăăȘăăŻăăăźæçäșșă§ăăăŹă·ăăæăăŠ' -cnvUsagetextllama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
build/bin/llama-cli -m 'DeepSeek-R1-Distill-Llama-70B-gguf' -n 128 -c 128 -p 'ăăȘăăŻăăăźæçäșșă§ăăăŹă·ăăæăăŠ' -cnvUsagetextllama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
build/bin/llama-cli -m 'DeepSeek-R1-Distill-Llama-70B-gguf' -n 128 -c 128 -p 'ăăȘăăŻăăăźæçäșșă§ăăăŹă·ăăæăăŠ' -cnvUsagetextllama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
build/bin/llama-cli -m 'DeepSeek-R1-Distill-Llama-70B-gguf' -n 128 -c 128 -p 'ăăȘăăŻăăăźæçäșșă§ăăăŹă·ăăæăăŠ' -cnvDeploy This Model
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