Text Generation
GGUF
English
nlp
llm
quantized
GGUF
imatrix
quantization
imat
static
16bit
8bit
6bit
5bit
4bit
3bit
2bit
1bit
Instructions to use legraphista/K2-IMat-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 legraphista/K2-IMat-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 legraphista/K2-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf legraphista/K2-IMat-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 legraphista/K2-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf legraphista/K2-IMat-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 legraphista/K2-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf legraphista/K2-IMat-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 legraphista/K2-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf legraphista/K2-IMat-GGUF:Q4_K_S
Use Docker
docker model run hf.co/legraphista/K2-IMat-GGUF:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use legraphista/K2-IMat-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "legraphista/K2-IMat-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "legraphista/K2-IMat-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/legraphista/K2-IMat-GGUF:Q4_K_S
- Ollama
How to use legraphista/K2-IMat-GGUF with Ollama:
ollama run hf.co/legraphista/K2-IMat-GGUF:Q4_K_S
- Unsloth Studio
How to use legraphista/K2-IMat-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 legraphista/K2-IMat-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 legraphista/K2-IMat-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for legraphista/K2-IMat-GGUF to start chatting
- Docker Model Runner
How to use legraphista/K2-IMat-GGUF with Docker Model Runner:
docker model run hf.co/legraphista/K2-IMat-GGUF:Q4_K_S
- Lemonade
How to use legraphista/K2-IMat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull legraphista/K2-IMat-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.K2-IMat-GGUF-Q4_K_S
List all available models
lemonade list
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -90,7 +90,7 @@ Link: [here](https://huggingface.co/legraphista/K2-IMat-GGUF/blob/main/imatrix.d
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| [K2.IQ2_XS.gguf](https://huggingface.co/legraphista/K2-IMat-GGUF/blob/main/K2.IQ2_XS.gguf) | IQ2_XS | 19.27GB | β
Available | π’ IMatrix | π¦ No
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| [K2.IQ2_XXS.gguf](https://huggingface.co/legraphista/K2-IMat-GGUF/blob/main/K2.IQ2_XXS.gguf) | IQ2_XXS | 17.47GB | β
Available | π’ IMatrix | π¦ No
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| [K2.IQ1_M.gguf](https://huggingface.co/legraphista/K2-IMat-GGUF/blob/main/K2.IQ1_M.gguf) | IQ1_M | 15.43GB | β
Available | π’ IMatrix | π¦ No
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| K2.IQ1_S | IQ1_S |
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## Downloading using huggingface-cli
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| 90 |
| [K2.IQ2_XS.gguf](https://huggingface.co/legraphista/K2-IMat-GGUF/blob/main/K2.IQ2_XS.gguf) | IQ2_XS | 19.27GB | β
Available | π’ IMatrix | π¦ No
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| 91 |
| [K2.IQ2_XXS.gguf](https://huggingface.co/legraphista/K2-IMat-GGUF/blob/main/K2.IQ2_XXS.gguf) | IQ2_XXS | 17.47GB | β
Available | π’ IMatrix | π¦ No
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| 92 |
| [K2.IQ1_M.gguf](https://huggingface.co/legraphista/K2-IMat-GGUF/blob/main/K2.IQ1_M.gguf) | IQ1_M | 15.43GB | β
Available | π’ IMatrix | π¦ No
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| [K2.IQ1_S.gguf](https://huggingface.co/legraphista/K2-IMat-GGUF/blob/main/K2.IQ1_S.gguf) | IQ1_S | 14.21GB | β
Available | π’ IMatrix | π¦ No
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## Downloading using huggingface-cli
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