Instructions to use VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF", filename="dictalm-3.0-24b-base-w4a16-q4_k_s.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 VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF 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 VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S
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 VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S
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 VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S
Use Docker
docker model run hf.co/VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S
- Ollama
How to use VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF with Ollama:
ollama run hf.co/VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S
- Unsloth Studio
How to use VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-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 VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-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 VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF with Docker Model Runner:
docker model run hf.co/VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S
- Lemonade
How to use VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull VRDate/DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.DictaLM-3.0-24B-Base-W4A16-Q4_K_S-GGUF-Q4_K_S
List all available models
lemonade list
- Xet hash:
- 6e7d40c4e5d7035420e1d3782762802b7699c68920289937e29af51317bba850
- Size of remote file:
- 13.5 GB
- SHA256:
- 8dffbf9843148e27ad80e477263a0528880a9b0d9c167c27d03da5d59c3e69e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.