Instructions to use Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: llama cli -hf Aliguinga01/rule_violation2:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: llama cli -hf Aliguinga01/rule_violation2:F16
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 Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: ./llama-cli -hf Aliguinga01/rule_violation2:F16
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 Aliguinga01/rule_violation2:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Aliguinga01/rule_violation2:F16
Use Docker
docker model run hf.co/Aliguinga01/rule_violation2:F16
- LM Studio
- Jan
- Ollama
How to use Aliguinga01/rule_violation2 with Ollama:
ollama run hf.co/Aliguinga01/rule_violation2:F16
- Unsloth Studio
How to use Aliguinga01/rule_violation2 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Aliguinga01/rule_violation2 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Aliguinga01/rule_violation2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Aliguinga01/rule_violation2 to start chatting
- Docker Model Runner
How to use Aliguinga01/rule_violation2 with Docker Model Runner:
docker model run hf.co/Aliguinga01/rule_violation2:F16
- Lemonade
How to use Aliguinga01/rule_violation2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Aliguinga01/rule_violation2:F16
Run and chat with the model
lemonade run user.rule_violation2-F16
List all available models
lemonade list
- Atomic Chat
| API_URL="${API_URL:-http://127.0.0.1:8080}" | |
| CHAT=( | |
| "Hello, Assistant." | |
| "Hello. How may I help you today?" | |
| "Please tell me the largest city in Europe." | |
| "Sure. The largest city in Europe is Moscow, the capital of Russia." | |
| ) | |
| INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions." | |
| trim() { | |
| shopt -s extglob | |
| set -- "${1##+([[:space:]])}" | |
| printf "%s" "${1%%+([[:space:]])}" | |
| } | |
| trim_trailing() { | |
| shopt -s extglob | |
| printf "%s" "${1%%+([[:space:]])}" | |
| } | |
| format_prompt() { | |
| echo -n "${INSTRUCTION}" | |
| printf "\n### Human: %s\n### Assistant: %s" "${CHAT[@]}" "$1" | |
| } | |
| tokenize() { | |
| curl \ | |
| --silent \ | |
| --request POST \ | |
| --url "${API_URL}/tokenize" \ | |
| --header "Content-Type: application/json" \ | |
| --data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \ | |
| | jq '.tokens[]' | |
| } | |
| N_KEEP=$(tokenize "${INSTRUCTION}" | wc -l) | |
| chat_completion() { | |
| PROMPT="$(trim_trailing "$(format_prompt "$1")")" | |
| DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{ | |
| prompt: ., | |
| temperature: 0.2, | |
| top_k: 40, | |
| top_p: 0.9, | |
| n_keep: $n_keep, | |
| n_predict: 256, | |
| cache_prompt: true, | |
| stop: ["\n### Human:"], | |
| stream: true | |
| }')" | |
| ANSWER='' | |
| while IFS= read -r LINE; do | |
| if [[ $LINE = data:* ]]; then | |
| CONTENT="$(echo "${LINE:5}" | jq -r '.content')" | |
| printf "%s" "${CONTENT}" | |
| ANSWER+="${CONTENT}" | |
| fi | |
| done < <(curl \ | |
| --silent \ | |
| --no-buffer \ | |
| --request POST \ | |
| --url "${API_URL}/completion" \ | |
| --header "Content-Type: application/json" \ | |
| --data-raw "${DATA}") | |
| printf "\n" | |
| CHAT+=("$1" "$(trim "$ANSWER")") | |
| } | |
| while true; do | |
| read -r -e -p "> " QUESTION | |
| chat_completion "${QUESTION}" | |
| done | |