x0000001/Qwen3-30B-A6B-16-Extreme-128k-context-Q6_K-GGUF (STATIC GGUF)

This model was converted to GGUF format from DavidAU/Qwen3-30B-A6B-16-Extreme-128k-context using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Not recommended for short context tasks:

All the notable open-source frameworks implement static YaRN,
which means the scaling factor remains constant regardless of input length,
potentially impacting performance
on shorter texts. We advise adding the rope_scaling configuration only when processing
long contexts is required.
It is also recommended to modify the factor as needed. For example, if the typical context length
for your application is 65,536 tokens, it would be better to set factor as 2.0.

    The default max_position_embeddings in config.json is set to 40,960.
This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens
for typical prompts, which is sufficient for most scenarios involving short text
processing. If the average context length does
not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario,
as it may potentially degrade
model performance.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo x0000001/Qwen3-30B-A6B-16-Extreme-128k-context-Q6_K-GGUF --hf-file qwen3-30b-a6b-16-extreme-128k-context-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo x0000001/Qwen3-30B-A6B-16-Extreme-128k-context-Q6_K-GGUF --hf-file qwen3-30b-a6b-16-extreme-128k-context-q6_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo x0000001/Qwen3-30B-A6B-16-Extreme-128k-context-Q6_K-GGUF --hf-file qwen3-30b-a6b-16-extreme-128k-context-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo x0000001/Qwen3-30B-A6B-16-Extreme-128k-context-Q6_K-GGUF --hf-file qwen3-30b-a6b-16-extreme-128k-context-q6_k.gguf -c 2048