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hwpoison89/gemma-1B-LinuxCLI-GGUF
gemma-1B-LinuxCLI-GGUF is a machine learning model from hwpoison89. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
I trained the base model using the dataaset 'hrsvrn/linux-commands-updated' using 25s steps (loss 0.012000) to give commands according user input.
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.gguf3.1 GB · 100%
From the Hugging Face model README
I trained the base model using the dataaset 'hrsvrn/linux-commands-updated' using 25s steps (loss 0.012000) to give commands according user input.
You are a helpful assistant that outputs Linux shell commands. You just provide the command that the user is requesting, not more. Just provide the output of the command and ignore commandsthat you do not have an example output for. If multiple commands aregiven then they will be separated by a ';' character and you need togive an example output for each command separated by a ';'.
vim ~/.bashrc
bind -x '"\C-o": llm_linux_cli' # Ctrl+O to run the model over current input line
llm_linux_cli() {
local salida
local USER_INPUT="$READLINE_LINE"
history -s "$READLINE_LINE"
SCRIPT=$(cat <<'EOF'
USER_INPUT=$(printf '%s' "$*" | sed 's/\\/\\\\/g; s/"/\\"/g')
if [ -z "$USER_INPUT" ]; then
echo "Uso: $0 \"Write something to the model\""
exit 1
fi
curl -s --url "http://localhost:8080/v1/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer no-key" \
-d "{
\"model\": \"gpt-3.5-turbo\",
\"temperature\":0.8,
\"top_p\":0.95,
\"min_p\":0.05,
\"top_k\":40,
\"messages\":[
{
\"role\":\"system\",
\"content\":\"You are a helpful assistant that outputs Linux shell commands. You just provide the command that the user is requesting, not more. Just provide the output of the command and ignore commands that you do not have an example output for. If multiple commands are given then they will be separated by a ';' character and you need togive an example output for each command separated by a ';'.\"
},
{
\"role\": \"user\",
\"content\":\"$USER_INPUT\"
}
]
}" | jq -r '.choices[0].message.content'
EOF
)
llm_output=$(bash -c "$SCRIPT" _ $USER_INPUT)
READLINE_LINE=$llm_output
READLINE_POINT=${#llm_output}
}
source ~/.bashrc
llama-server -m gemma-1B-LinuxCLI-GGUF.gguf
This gemma3_text model was trained 2x faster with Unsloth and Huggingface's TRL library.