Uncensored_Qwen
Collection
6 items • Updated
How to use ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M # Run inference directly in the terminal: llama cli -hf ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M # Run inference directly in the terminal: llama cli -hf ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b: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 ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b: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 ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M
docker model run hf.co/ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M
How to use ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M
How to use ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b with Ollama:
ollama run hf.co/ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M
How to use ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b 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 ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b 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 ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b to start chatting
How to use ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b with Docker Model Runner:
docker model run hf.co/ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M
How to use ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:Q4_K_M
lemonade run user.Uncensored_Replete_Coder_Qwen2_1.5b-Q4_K_M
lemonade list
docker model run hf.co/ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b:4-bit
8-bit
16-bit
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ICEPVP8977/Uncensored_Replete_Coder_Qwen2_1.5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'