Summarization
GGUF
English
llama
text-summarization
text2text-generation
news
articles
minibase
standard-model
4096-context
Eval Results (legacy)
Instructions to use Minibase/Content-Preview-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Minibase/Content-Preview-Generator with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Minibase/Content-Preview-Generator # Run inference directly in the terminal: llama cli -hf Minibase/Content-Preview-Generator
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Minibase/Content-Preview-Generator # Run inference directly in the terminal: llama cli -hf Minibase/Content-Preview-Generator
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Minibase/Content-Preview-Generator # Run inference directly in the terminal: ./llama-cli -hf Minibase/Content-Preview-Generator
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Minibase/Content-Preview-Generator # Run inference directly in the terminal: ./build/bin/llama-cli -hf Minibase/Content-Preview-Generator
Use Docker
docker model run hf.co/Minibase/Content-Preview-Generator
- LM Studio
- Jan
- Ollama
How to use Minibase/Content-Preview-Generator with Ollama:
ollama run hf.co/Minibase/Content-Preview-Generator
- Unsloth Desktop
- Docker Model Runner
How to use Minibase/Content-Preview-Generator with Docker Model Runner:
docker model run hf.co/Minibase/Content-Preview-Generator
- Lemonade
How to use Minibase/Content-Preview-Generator with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Minibase/Content-Preview-Generator
Run and chat with the model
lemonade run user.Content-Preview-Generator-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| # Benchmark Configuration for Summarizer-Standard Model | |
| # Standard summarization model for text summarization tasks | |
| model: | |
| name: "summarizer-standard" | |
| base_url: "http://127.0.0.1:8000" | |
| temperature: 0.3 # Lower temperature for consistent summaries | |
| max_tokens: 256 # Reasonable summary length | |
| timeout: 45 # Allow time for summarization | |
| datasets: | |
| - name: "cnn_dailymail" | |
| file: "datasets/cnn_dailymail_sample.jsonl" | |
| sample_size: 100 # Reasonable sample size for benchmarking | |
| instruction: "Summarize the following article in 2-3 sentences." | |
| input_field: "article" | |
| expected_field: "highlights" | |
| evaluation: | |
| rouge_threshold: 0.3 # Lenient ROUGE score threshold | |
| semantic_preservation_min: 0.4 # Lenient semantic similarity | |
| length_ratio_min: 0.1 # Minimum compression ratio | |
| length_ratio_max: 0.8 # Maximum compression ratio | |
| output: | |
| results_dir: "results" | |
| include_raw_responses: false | |
| model_size_gb: 0.369 # From file size check (369MB) | |
| cnn_dailymail: | |
| source_url: "https://huggingface.co/datasets/cnn_dailymail" | |
| max_samples: 2000 | |