Skywork/SkyPile-150B
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How to use tensorblock/Sailor-7B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tensorblock/Sailor-7B-GGUF", filename="Sailor-7B-Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
How to use tensorblock/Sailor-7B-GGUF with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/Sailor-7B-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/Sailor-7B-GGUF:Q2_K
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/Sailor-7B-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/Sailor-7B-GGUF:Q2_K
# 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 tensorblock/Sailor-7B-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/Sailor-7B-GGUF:Q2_K
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 tensorblock/Sailor-7B-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/Sailor-7B-GGUF:Q2_K
docker model run hf.co/tensorblock/Sailor-7B-GGUF:Q2_K
How to use tensorblock/Sailor-7B-GGUF with Ollama:
ollama run hf.co/tensorblock/Sailor-7B-GGUF:Q2_K
How to use tensorblock/Sailor-7B-GGUF with Unsloth Studio:
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 tensorblock/Sailor-7B-GGUF to start chatting
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 tensorblock/Sailor-7B-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tensorblock/Sailor-7B-GGUF to start chatting
How to use tensorblock/Sailor-7B-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/Sailor-7B-GGUF:Q2_K
How to use tensorblock/Sailor-7B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/Sailor-7B-GGUF:Q2_K
lemonade run user.Sailor-7B-GGUF-Q2_K
lemonade list
This repo contains GGUF format model files for sail/Sailor-7B.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
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<|im_start|>system
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<|im_start|>user
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<|im_start|>assistant
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Sailor-7B-Q2_K.gguf | Q2_K | 3.104 GB | smallest, significant quality loss - not recommended for most purposes |
| Sailor-7B-Q3_K_S.gguf | Q3_K_S | 3.569 GB | very small, high quality loss |
| Sailor-7B-Q3_K_M.gguf | Q3_K_M | 3.919 GB | very small, high quality loss |
| Sailor-7B-Q3_K_L.gguf | Q3_K_L | 4.218 GB | small, substantial quality loss |
| Sailor-7B-Q4_0.gguf | Q4_0 | 4.512 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Sailor-7B-Q4_K_S.gguf | Q4_K_S | 4.543 GB | small, greater quality loss |
| Sailor-7B-Q4_K_M.gguf | Q4_K_M | 4.767 GB | medium, balanced quality - recommended |
| Sailor-7B-Q5_0.gguf | Q5_0 | 5.399 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Sailor-7B-Q5_K_S.gguf | Q5_K_S | 5.399 GB | large, low quality loss - recommended |
| Sailor-7B-Q5_K_M.gguf | Q5_K_M | 5.531 GB | large, very low quality loss - recommended |
| Sailor-7B-Q6_K.gguf | Q6_K | 6.342 GB | very large, extremely low quality loss |
| Sailor-7B-Q8_0.gguf | Q8_0 | 8.212 GB | very large, extremely low quality loss - not recommended |
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/Sailor-7B-GGUF --include "Sailor-7B-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/Sailor-7B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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