Instructions to use FriskyFennec/Ling-3.0-tiny-base-30T-GGUF 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 FriskyFennec/Ling-3.0-tiny-base-30T-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 FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M
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 FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M
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 FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M
Use Docker
docker model run hf.co/FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use FriskyFennec/Ling-3.0-tiny-base-30T-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FriskyFennec/Ling-3.0-tiny-base-30T-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FriskyFennec/Ling-3.0-tiny-base-30T-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M
- Ollama
How to use FriskyFennec/Ling-3.0-tiny-base-30T-GGUF with Ollama:
ollama run hf.co/FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use FriskyFennec/Ling-3.0-tiny-base-30T-GGUF with Docker Model Runner:
docker model run hf.co/FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M
- Lemonade
How to use FriskyFennec/Ling-3.0-tiny-base-30T-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FriskyFennec/Ling-3.0-tiny-base-30T-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Ling-3.0-tiny-base-30T-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Ling-3.0-tiny-base-30T
A GGUF variant of inclusionAI's Ling-3.0-tiny-base-30T model, suitable for base/foundational model experiments and text completion use cases.
The model was converted & quantized using llama.cpp's build 0.4.0-dev.
Examples
Running via llama.cpp
Model's tested with,
llama-server.exe --model Ling-3.0-tiny-base-30T-BF16.gguf --ctx-size 8192 --gpu-layers 256 --main-gpu 0 --flash-attn auto --mlock --timeout 600 --host 127.0.0.1 --port 8080
Note: The model was trained with 8192 context length (config.json, developer's clarification). For the GGUF variant of the model with full context length (256K), refer to FriskyFennec/Ling-3.0-tiny-base-midtrain-GGUF.
Output
Samplers:
- Temperature: 0.7
- Top P: 0.95
- Top K: 40
- Repetition Penalty: 1.1
- Repetition Penalty Range: 256
Input,
Once upon a time,
Output 1,
in the world of music, there were two genres that seemed to have little in common: hip hop and rock. However, as the years went on, these two seemingly disparate worlds began to merge, creating a unique sound that captivated audiences worldwide.
Output 2,
the internet was a place where people shared their photos and videos with friends and family. But then, someone came up with an idea that would change everything: sharing pictures of yourself on social media.
At first, it seemed like a harmless way to
Output 3,
in the magical land of Minecraft, there lived a character known as The Wither. The Wither is a powerful and mysterious creature that can be found deep underground, often in dark caves. It looks like a giant skeleton with three eyes and is covered
License
The model inherits the original MIT license from inclusionAI/Ling-3.0-tiny-base-30T.
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Model tree for FriskyFennec/Ling-3.0-tiny-base-30T-GGUF
Base model
inclusionAI/Ling-3.0-tiny-base-30T