How to use from
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 bala00712200502/bala02122005
# Run inference directly in the terminal:
llama cli -hf bala00712200502/bala02122005
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bala00712200502/bala02122005
# Run inference directly in the terminal:
llama cli -hf bala00712200502/bala02122005
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 bala00712200502/bala02122005
# Run inference directly in the terminal:
./llama-cli -hf bala00712200502/bala02122005
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 bala00712200502/bala02122005
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bala00712200502/bala02122005
Use Docker
docker model run hf.co/bala00712200502/bala02122005
Quick Links

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

sha256-633fc5be925f9a484b61d6f9b9a78021eeb462100bd557309f01ba84cac26adf-gguf GGUF

This is a quantized GGUF version of the model for use with llama.cpp or Ollama. Original model file was: sha256-633fc5be925f9a484b61d6f9b9a78021eeb462100bd557309f01ba84cac26adf (Full path on source machine for reference: C:\Users\balak.ollama\models\blobs\sha256-633fc5be925f9a484b61d6f9b9a78021eeb462100bd557309f01ba84cac26adf) Uploaded by user: bala00712200502

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GGUF
Model size
4B params
Architecture
phi3
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