saucam/aqua-qwen-0.1-110B-GGUF

This is GGUF model of saucam/aqua-qwen-0.1-110B

Usage

Download the 2 files and merge using llama.cpp.

gguf-split --merge aqua-qwen-0.1-110B-Q4_K_M-00001-of-00002.gguf aqua-qwen-0.1-110B-Q4_K_M.gguf

Then use the single generated file like below:

$ ./main -m aqua-qwen-0.1-110B-Q4_K_M.gguf -p "<|im_start|>user\nHow are you?<|im_end|>\n<|im_start|>assistant" -n 400 -e
Log start
main: build = 2874 (e0f55618)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed  = 1715672499
llama_model_loader: loaded meta data with 20 key-value pairs and 963 tensors from aqua-qwen-0.1-110B
-Q4_K_M.gguf (version GGUF V3 (latest))
...
...
sampling order: 
CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature 
generate: n_ctx = 512, n_batch = 2048, n_predict = 400, n_keep = 0


,<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
I am an AI, I do not have feelings. How can I assist you?<|im_end|> [end of text]

llama_print_timings:        load time =    4065.12 ms
llama_print_timings:      sample time =       1.70 ms /    19 runs   (    0.09 ms per token, 11150.23 tokens per second)
llama_print_timings: prompt eval time =    2898.40 ms /    12 tokens (  241.53 ms per token,     4.14 tokens per second)
llama_print_timings:        eval time =  178067.55 ms /    18 runs   ( 9892.64 ms per token,     0.10 tokens per second)
llama_print_timings:       total time =  181014.78 ms /    30 tokens
Log end