DeepSeek-R1-Distill-Llama-70B-lecore-GGUF

GGUF builds of deepseek-ai/DeepSeek-R1-Distill-Llama-70B for llama.cpp, Ollama and LM Studio. These are companions to the MLX export staccs/DeepSeek-R1-Distill-Llama-70B-lecore-mlx, which has the measured perplexity numbers.

The lecore-mix file is quantized from the original bf16 weights with llama.cpp K-quants. The bit width of each tensor follows leCore's measured (KL-damage) allocation, so its numbers are close to, but not identical to, the MLX export. Some tensor widths are not multiples of 256, so llama.cpp may fall back to a compatible type for those tensors.

Usage

Ollama: download a file, then

printf 'FROM ./DeepSeek-R1-Distill-Llama-70B-Q4_K_M.gguf
' > Modelfile && ollama create deepseek-r1-distill-llama-70b-lecore-gguf -f Modelfile && ollama run deepseek-r1-distill-llama-70b-lecore-gguf

or pull straight from the Hub with ollama run hf.co/staccs/DeepSeek-R1-Distill-Llama-70B-lecore-GGUF:Q4_K_M

LM Studio: search for staccs/DeepSeek-R1-Distill-Llama-70B-lecore-GGUF in the model browser and pick a file. On a 36 GB Mac, the lecore-mix file is the one that fits.

llama.cpp

llama-cli -hf staccs/DeepSeek-R1-Distill-Llama-70B-lecore-GGUF -p "Hello" -n 256

Benchmarks (lm-eval-harness 0.4.9, run on the shipped weights dequantized to bf16 on GPU)

MMLU 5-shot on the first 10 questions of each of the 57 subjects; GSM8K 5-shot on the first 100 test problems (greedy, 320 new tokens, no chat template). These are subsets, so expect about ±2–4 points of sampling noise.

License

Base model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B, released by DeepSeek under the MIT License. That model is fine-tuned from Llama-3.3-70B-Instruct, so the Llama 3.3 Community License Agreement also applies (https://www.llama.com/llama3_3/license/), including its Acceptable Use Policy. Built with Llama. Only quantization was done here: no retraining.

Related builds

Same base model, other formats in the lecore set: