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
| import time | |
| import argparse | |
| from transformers import AutoTokenizer | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") | |
| parser.add_argument("--fname-tok", help="path to a text file to tokenize", required=True) | |
| args = parser.parse_args() | |
| dir_tokenizer = args.dir_tokenizer | |
| fname_tok = args.fname_tok | |
| tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer) | |
| print('tokenizing file: ', fname_tok) # noqa: NP100 | |
| fname_out = fname_tok + '.tok' | |
| with open(fname_tok, 'r', encoding='utf-8') as f: | |
| lines = f.readlines() | |
| s = ''.join(lines) | |
| t_start = time.time() | |
| res = tokenizer.encode(s, add_special_tokens=False) | |
| t_end = time.time() | |
| print('\nmain : tokenized in', "{:.3f}".format(1000.0 * (t_end - t_start)), 'ms (py)') # noqa: NP100 | |
| with open(fname_out, 'w', encoding='utf-8') as f: | |
| for x in res: | |
| # LLaMA v3 for some reason strips the space for these tokens (and others) | |
| # if x == 662: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # elif x == 1174: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # elif x == 2564: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # elif x == 758: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # elif x == 949: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # elif x == 5354: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # else: | |
| # f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n') | |
| # f.write(str(x) + ' \'' + tokenizer.decode(x).strip() + '\'\n') | |
| f.write(str(x) + '\n') | |
| print('len(res): ', len(res)) # noqa: NP100 | |
| print('len(lines): ', len(lines)) # noqa: NP100 | |
| print('results written to: ', fname_out) # noqa: NP100 | |