issue-writer
Collection
Bilingual (EN/TR) synthetic dataset generator and LoRA fine-tuning pipeline that turns raw product input into well-formed issue tracker entries • 4 items • Updated
How to use fport/issue-writer-gemma4-gguf with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf fport/issue-writer-gemma4-gguf:BF16 # Run inference directly in the terminal: llama cli -hf fport/issue-writer-gemma4-gguf:BF16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf fport/issue-writer-gemma4-gguf:BF16 # Run inference directly in the terminal: llama cli -hf fport/issue-writer-gemma4-gguf:BF16
# 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 fport/issue-writer-gemma4-gguf:BF16 # Run inference directly in the terminal: ./llama-cli -hf fport/issue-writer-gemma4-gguf:BF16
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 fport/issue-writer-gemma4-gguf:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf fport/issue-writer-gemma4-gguf:BF16
docker model run hf.co/fport/issue-writer-gemma4-gguf:BF16
How to use fport/issue-writer-gemma4-gguf with Ollama:
ollama run hf.co/fport/issue-writer-gemma4-gguf:BF16
How to use fport/issue-writer-gemma4-gguf with Docker Model Runner:
docker model run hf.co/fport/issue-writer-gemma4-gguf:BF16
How to use fport/issue-writer-gemma4-gguf with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fport/issue-writer-gemma4-gguf:BF16
lemonade run user.issue-writer-gemma4-gguf-BF16
lemonade list
This model was finetuned and converted to GGUF format using Unsloth.
Example usage:
llama-cli -hf fport/issue-writer-gemma4-gguf --jinjallama-mtmd-cli -hf fport/issue-writer-gemma4-gguf --jinjagemma-4-E4B-it.Q4_K_M.ggufgemma-4-E4B-it.BF16-mmproj.ggufImportant: Ollama currently does not support separate mmproj files for vision models.
To create an Ollama model from this vision model:
Modelfile in the same directory as the finetuned bf16 merged modelollama create model_name -f ./Modelfile
(Replace model_name with your desired name)This will create a unified bf16 model that Ollama can use.
This was trained 2x faster with Unsloth

4-bit