Instructions to use MosRat/got_decoder_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use MosRat/got_decoder_gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MosRat/got_decoder_gguf", filename="got_decoder-q2_k.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use MosRat/got_decoder_gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MosRat/got_decoder_gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MosRat/got_decoder_gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MosRat/got_decoder_gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MosRat/got_decoder_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 MosRat/got_decoder_gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MosRat/got_decoder_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 MosRat/got_decoder_gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MosRat/got_decoder_gguf:Q4_K_M
Use Docker
docker model run hf.co/MosRat/got_decoder_gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use MosRat/got_decoder_gguf with Ollama:
ollama run hf.co/MosRat/got_decoder_gguf:Q4_K_M
- Unsloth Studio new
How to use MosRat/got_decoder_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 MosRat/got_decoder_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 MosRat/got_decoder_gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MosRat/got_decoder_gguf to start chatting
- Docker Model Runner
How to use MosRat/got_decoder_gguf with Docker Model Runner:
docker model run hf.co/MosRat/got_decoder_gguf:Q4_K_M
- Lemonade
How to use MosRat/got_decoder_gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MosRat/got_decoder_gguf:Q4_K_M
Run and chat with the model
lemonade run user.got_decoder_gguf-Q4_K_M
List all available models
lemonade list
Upload 5 files
Browse files- .gitattributes +5 -0
- got_decoder-q3_k_l.gguf +3 -0
- got_decoder-q5_0.gguf +3 -0
- got_decoder-q5_k_m.gguf +3 -0
- got_decoder-q5_k_s.gguf +3 -0
- got_decoder-q6_k.gguf +3 -0
.gitattributes
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@@ -39,3 +39,8 @@ got_decoder-q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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got_decoder-q4_k_s.gguf filter=lfs diff=lfs merge=lfs -text
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got_decoder-q4_k_s.gguf filter=lfs diff=lfs merge=lfs -text
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got_decoder-q3_k_l.gguf
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size 352211840
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got_decoder-q5_k_s.gguf
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oid sha256:bbd9db3c371f484af09897c664debe203118c250d98178300017c046e12aeca7
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oid sha256:ba7caf152e9174bc0b23cb8fca65aaa040a1f58991f7207cacd62e5cc31d7a68
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size 386888576
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