bigcode/the-stack-smol-xl
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How to use jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0 # Run inference directly in the terminal: llama cli -hf jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0
# 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 jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0
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 jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0
docker model run hf.co/jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0
How to use jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF with Ollama:
ollama run hf.co/jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0
How to use jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF with Docker Model Runner:
docker model run hf.co/jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0
How to use jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jukofyork/DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF:Q4_0
lemonade run user.DeepSeek-R1-DRAFT-0.6B-v3.0-GGUF-Q4_0
lemonade list
A 0.6B parameter draft (speculative decoding) model for use with DeepSeek-R1-0528 and DeepSeek-R1.
See DeepSeek-R1-DRAFT-0.6B-v3.0 for the models in transformers format, and a detailed explanation of how the model was created.
I've included the Q4_0 quants for 4 different context lengths:
Qwen2.5-0.5B doesn't allow for any of the other 4-bit quants to be made (and experimentation has shown using more or less than 4-bits for speculative decoding is a waste of time anwyay).llama.cpp using "static-YaRN" the scaling factor remains constant regardless of input length! Only use the longer context versions when processing long contexts is required...4-bit