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

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

rule_violation2 / llama.cpp /examples /training /README.md

Aliguinga01

Upload folder using huggingface_hub

4d35814 verified 10 months ago

preview code

Raw

History Blame Contribute Delete

911 Bytes

llama.cpp/examples/training

This directory contains examples related to language model training using llama.cpp/GGML. So far finetuning is technically functional (for FP32 models and limited hardware setups) but the code is very much WIP. Finetuning of Stories 260K and LLaMA 3.2 1b seems to work with 24 GB of memory. For CPU training, compile llama.cpp without any additional backends such as CUDA. For CUDA training, use the maximum number of GPU layers.

Proof of concept:

export model_name=llama_3.2-1b && export quantization=f32
./build/bin/llama-finetune --file wikitext-2-raw/wiki.test.raw -ngl 999 --model models/${model_name}-${quantization}.gguf -c 512 -b 512 -ub 512
./build/bin/llama-perplexity --file wikitext-2-raw/wiki.test.raw -ngl 999 --model finetuned-model.gguf

The perplexity value of the finetuned model should be lower after training on the test set for 2 epochs.