Instructions to use cstr/bert-base-NER-GGUF 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 cstr/bert-base-NER-GGUF 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 cstr/bert-base-NER-GGUF:F32 # Run inference directly in the terminal: llama cli -hf cstr/bert-base-NER-GGUF:F32
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cstr/bert-base-NER-GGUF:F32 # Run inference directly in the terminal: llama cli -hf cstr/bert-base-NER-GGUF:F32
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 cstr/bert-base-NER-GGUF:F32 # Run inference directly in the terminal: ./llama-cli -hf cstr/bert-base-NER-GGUF:F32
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 cstr/bert-base-NER-GGUF:F32 # Run inference directly in the terminal: ./build/bin/llama-cli -hf cstr/bert-base-NER-GGUF:F32
Use Docker
docker model run hf.co/cstr/bert-base-NER-GGUF:F32
- LM Studio
- Jan
- Ollama
How to use cstr/bert-base-NER-GGUF with Ollama:
ollama run hf.co/cstr/bert-base-NER-GGUF:F32
- Unsloth Desktop
- Docker Model Runner
How to use cstr/bert-base-NER-GGUF with Docker Model Runner:
docker model run hf.co/cstr/bert-base-NER-GGUF:F32
- Lemonade
How to use cstr/bert-base-NER-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cstr/bert-base-NER-GGUF:F32
Run and chat with the model
lemonade run user.bert-base-NER-GGUF-F32
List all available models
lemonade list
- Atomic Chat
Commit History
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docs: add provenance / EU AI Act Art. 53 note
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importance matrix (calibration)
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A/B summary (f1 vs gold)
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iq4_xs +imatrix (f1_vs_full-precision=1.0000)
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q4_k +imatrix (f1_vs_full-precision=0.9474)
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Upload bert-ner-ref.gguf with huggingface_hub
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Add model card README
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Add bert-base-ner-q4_k.gguf
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Add bert-base-ner-q8_0.gguf
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Add bert-base-ner-f32.gguf
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initial commit
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