How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B-GGUF:
# Run inference directly in the terminal:
llama-cli -hf alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B-GGUF:
# Run inference directly in the terminal:
llama-cli -hf alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B-GGUF:
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 alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B-GGUF:
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 alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B-GGUF:
Use Docker
docker model run hf.co/alamios/DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B-GGUF:
Quick Links

DeepSeek-R1-DRAFT-Qwen2.5-Coder-0.5B-GGUF

Updated to v1

This model is trained on CODE outputs of deepseek-ai/DeepSeek-R1-Distill-Qwen-32B and is meant to be used only as draft model for speculative decoding.

It's specifically intended for users of 3090/4090, allowing you to run the DeepSeek-R1-Distill-Qwen-32B-Q4_K_M GGUF version with 16k context and speeding up generation without sacrificing more context length or model quality.

Data info

The data consists of code tasks collected from various datasets. It has been trained for 2 epochs on 2.5k unique examples, for a total of 7.6 million tokens per epoch.

Since data generation was done using spare GPU time, I may publish a further trained version later.

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Architecture
qwen2
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