huihui-ai/Kimi-K2-Instruct-GGUF

This model converted from unsloth/Kimi-K2-Instruct-BF16 to GGUF.
Here we simply provide the conversion command and related information about ollama.

BF16 to f16.gguf

1. Use the llama.cpp conversion program to convert Kimi-K2-Instruct-BF16 to gguf format, requires an additional approximately 2.1 TB of space.

python convert_hf_to_gguf.py /home/admin/models/unsloth/Kimi-K2-Instruct-BF16 --outfile /home/admin/models/unsloth/Kimi-K2-Instruct-BF16/ggml-model-f16.gguf --outtype f16

2. Use the llama.cpp quantitative program to quantitative model (llama-quantize needs to be compiled.), other quant option. Convert first Q2_K, requires an additional approximately 347 GB of space.

llama-quantize /home/admin/models/unsloth/Kimi-K2-Instruct-BF16/ggml-model-f16.gguf  /home/admin/models/unsloth/Kimi-K2-Instruct-BF16/ggml-model-Q2_K.gguf Q2_K

3. Use llama-cli to test.

llama-cli -m /home/admin/models/unsloth/Kimi-K2-Instruct-BF16/ggml-model-Q2_K.gguf -n 2048

Use with ollama

The current version (0.9.6) of Ollama, due to LLAMA_MAX_EXPERTS being set to 256 in llama-hparams.h, requires manual modification to 384 and recompilation to run properly.

-- #define LLAMA_MAX_EXPERTS 256  // DeepSeekV3
++ #define LLAMA_MAX_EXPERTS 384  // Kimi-K2-Instruct

How to recompile ollama, please refer to Development

You can use huihui_ai/kimi-k2:1026b-Q2_K directly,

ollama run huihui_ai/huihui_ai/kimi-k2:1026b-Q2_K

Parameter description

1. num_gpu
The value of num_gpu inside the model is 1, which means it defaults to loading one layer. All others will be loaded into CPU memory. You can modify num_gpu according to your GPU configuration.

/set parameter num_gpu 2

2. num_thread
"num_thread" refers to the number of cores in your computer, and it's recommended to use half of that, Otherwise, the CPU will be at 100%.

/set parameter num_thread 32

3. num_ctx
"num_ctx" for ollama refers to the number of context slots or the number of contexts the model can maintain during inference.

/set parameter num_ctx 4096

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