Instructions to use Aliguinga01/rule_violation2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Aliguinga01/rule_violation2 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Aliguinga01/rule_violation2", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Aliguinga01/rule_violation2 with llama.cpp:
Install from brew
brew install llama.cpp # 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
Install from WinGet (Windows)
winget install llama.cpp # 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
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 new
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
| layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; | |
| layout (binding = 0) readonly buffer A {block_q4_0 data_a[];}; | |
| layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; | |
| void main() { | |
| const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; | |
| const uint tid = gl_LocalInvocationID.x % 64; | |
| const uint il = tid/32; | |
| const uint ir = tid%32; | |
| const uint ib = 32*i + ir; | |
| if (ib >= p.nel / 32) { | |
| return; | |
| } | |
| const uint q_idx = 8*il; | |
| const uint b_idx = 1024*i + 32*ir + q_idx; | |
| const float d = float(data_a[ib].d); | |
| [[unroll]] for (uint l = 0; l < 8; ++l) { | |
| data_b[b_idx + l + 0] = D_TYPE(d * ((data_a[ib].qs[q_idx + l] & 0xF) - 8.0f)); | |
| data_b[b_idx + l + 16] = D_TYPE(d * ((data_a[ib].qs[q_idx + l] >> 4) - 8.0f)); | |
| } | |
| } | |