HuggingFaceH4/deita-10k-v0-sft
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How to use LoneStriker/zephyr-7b-gemma-sft-v0.1-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 LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M
# 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 LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M
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 LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M
docker model run hf.co/LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M
How to use LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF with Ollama:
ollama run hf.co/LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M
How to use LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF with Docker Model Runner:
docker model run hf.co/LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M
How to use LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LoneStriker/zephyr-7b-gemma-sft-v0.1-GGUF:Q4_K_M
lemonade run user.zephyr-7b-gemma-sft-v0.1-GGUF-Q4_K_M
lemonade list
This model is a fine-tuned version of google/gemma-7b on the HuggingFaceH4/deita-10k-v0-sft dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9482 | 1.0 | 299 | 0.9848 |
| 0.8139 | 2.0 | 599 | 0.9610 |
| 0.722 | 2.99 | 897 | 0.9732 |
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Base model
google/gemma-7b