Instructions to use hellork/law-chat-IQ4_NL-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use hellork/law-chat-IQ4_NL-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="hellork/law-chat-IQ4_NL-GGUF", filename="law-chat-iq4_nl-imat.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use hellork/law-chat-IQ4_NL-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf hellork/law-chat-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: llama-cli -hf hellork/law-chat-IQ4_NL-GGUF:IQ4_NL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf hellork/law-chat-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: llama-cli -hf hellork/law-chat-IQ4_NL-GGUF:IQ4_NL
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 hellork/law-chat-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: ./llama-cli -hf hellork/law-chat-IQ4_NL-GGUF:IQ4_NL
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 hellork/law-chat-IQ4_NL-GGUF:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama-cli -hf hellork/law-chat-IQ4_NL-GGUF:IQ4_NL
Use Docker
docker model run hf.co/hellork/law-chat-IQ4_NL-GGUF:IQ4_NL
- LM Studio
- Jan
- vLLM
How to use hellork/law-chat-IQ4_NL-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hellork/law-chat-IQ4_NL-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hellork/law-chat-IQ4_NL-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hellork/law-chat-IQ4_NL-GGUF:IQ4_NL
- Ollama
How to use hellork/law-chat-IQ4_NL-GGUF with Ollama:
ollama run hf.co/hellork/law-chat-IQ4_NL-GGUF:IQ4_NL
- Unsloth Studio
How to use hellork/law-chat-IQ4_NL-GGUF 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 hellork/law-chat-IQ4_NL-GGUF 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 hellork/law-chat-IQ4_NL-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for hellork/law-chat-IQ4_NL-GGUF to start chatting
- Docker Model Runner
How to use hellork/law-chat-IQ4_NL-GGUF with Docker Model Runner:
docker model run hf.co/hellork/law-chat-IQ4_NL-GGUF:IQ4_NL
- Lemonade
How to use hellork/law-chat-IQ4_NL-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hellork/law-chat-IQ4_NL-GGUF:IQ4_NL
Run and chat with the model
lemonade run user.law-chat-IQ4_NL-GGUF-IQ4_NL
List all available models
lemonade list
hellork/law-chat-IQ4_NL-GGUF
This model was converted to GGUF format from AdaptLLM/law-chat using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo hellork/law-chat-IQ4_NL-GGUF --hf-file law-chat-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo hellork/law-chat-IQ4_NL-GGUF --hf-file law-chat-iq4_nl-imat.gguf -c 2048
The Ship's Computer:
Interact with this model by speaking to it. Lean, fast, & private, networked speech to text, AI images, multi-modal voice chat, control apps, webcam, and sound with less than 4GiB of VRAM.
git clone -b main --single-branch https://github.com/themanyone/whisper_dictation.git
pip install -r whisper_dictation/requirements.txt
git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
GGML_CUDA=1 make -j # assuming CUDA is available. see docs
ln -s server ~/.local/bin/whisper_cpp_server # (just put it somewhere in $PATH)
# -ngl option assums AI accelerator like CUDA is available
llama-server --hf-repo hellork/law-chat-IQ4_NL-GGUF --hf-file law-chat-iq4_nl-imat.gguf -c 2048 -ngl 17 --port 8888
whisper_cpp_server -l en -m models/ggml-tiny.en.bin --port 7777
cd whisper_dictation
./whisper_cpp_client.py
See the docs for tips on integrating with llama.cpp server, enabling the computer to talk back, draw AI images, carry out voice commands, and other features.
Install Llama.cpp via git:
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo hellork/law-chat-IQ4_NL-GGUF --hf-file law-chat-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo hellork/law-chat-IQ4_NL-GGUF --hf-file law-chat-iq4_nl-imat.gguf -c 2048
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Model tree for hellork/law-chat-IQ4_NL-GGUF
Base model
AdaptLLM/law-chatDatasets used to train hellork/law-chat-IQ4_NL-GGUF
GAIR/lima
EleutherAI/pile
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard53.410
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard76.160
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard50.240
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard43.530
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard75.450
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard18.500