yahma/alpaca-cleaned
Viewer • Updated • 51.8k • 29.4k • 833
How to use Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF", filename="Qwen3-8B-Base.F16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
How to use Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M
# 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 Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M
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 Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M
docker model run hf.co/Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M
How to use Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF with Ollama:
ollama run hf.co/Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M
How to use Alybit/Qwen3-8B-Instruct-Alpaca-3k-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 Alybit/Qwen3-8B-Instruct-Alpaca-3k-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 Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF to start chatting
How to use Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF with Docker Model Runner:
docker model run hf.co/Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M
How to use Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF:Q4_K_M
lemonade run user.Qwen3-8B-Instruct-Alpaca-3k-GGUF-Q4_K_M
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)This model was finetuned with the first 3000 examples of Alpaca Cleaned.
This model was finetuned and converted to GGUF format using Unsloth.
Example usage:
./llama.cpp/llama-cli -hf Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF --jinja./llama.cpp/llama-mtmd-cli -hf Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF --jinjaQwen3-8B-Base.F16.ggufQwen3-8B-Base.Q8_0.ggufQwen3-8B-Base.Q4_K_M.gguf
This was trained 2x faster with Unsloth

4-bit
8-bit
16-bit
Base model
Qwen/Qwen3-8B-Base
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Alybit/Qwen3-8B-Instruct-Alpaca-3k-GGUF", filename="", )