japanese-stablelm-instruct-gamma-7B-GGUF

2.1K
10
1 language
Q4
license:apache-2.0
by
TheBloke
Language Model
OTHER
7B params
New
2K downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
16GB+ RAM
Mobile
Laptop
Server
Quick Summary

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z) Japanese StableLM Instruct Gamma 7B - GGUF - Model creator: Stability AI...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
7GB+ RAM

Training Data Analysis

🟡 Average (5.3/10)

Researched training datasets used by japanese-stablelm-instruct-gamma-7B-GGUF with quality assessment

Specialized For

code
general
science
multilingual

Training Datasets (2)

the pile
🟢 8/10
code
general
science
multilingual
Key Strengths
  • •Deliberate Diversity: Explicitly curated to include diverse content types (academia, code, Q&A, book...
  • •Documented Quality: Each component dataset is thoroughly documented with rationale for inclusion, en...
  • •Epoch Weighting: Component datasets receive different training epochs based on perceived quality, al...
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...

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

On the command line, including multiple files at oncebash
pip3 install huggingface-hub
bash
pip3 install hf_transfer
How to load this model in Python code, using ctransformersbash
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers

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