build/bin/llama-cli throws SIGSEGV, Segmentation fault when using a TL2 GGUF model

To reproduce on a x86 architecture:

1. python3 setup_env.py --hf-repo HF1BitLLM/Llama3-8B-1.58-100B-tokens -q tl2
2. python3 run_inference.py -m models/bitnet_b1_58-large/ggml-model-tl2.gguf -p "Hi are you a LLM?" -n 6 -temp 0

Result:
Loading model... |Error occurred while running command: Command '['build/bin/llama-cli', '-m', 'models/bitnet_b1_58-large/ggml-model-tl2.gguf', '-n', '6', '-t', '2', '-p', 'Hi are you a LLM?', '-ngl', '0', '-c', '2048', '--temp', '0.0']' died with <Signals.SIGSEGV: 11>.

Compiling llama.cpp in debug and run llama-cli in gdb I get this back trace:
#0 0x0000000000000000 in ?? ()
#1 0x00007ffff5e0bde9 in ggml_compute_forward_mul_mat_one_chunk (params=0x7ffffffee9a0, dst=0x23f0730, type=GGML_TYPE_TL2, num_rows_per_vec_dot=0,
ir0_start=0, ir0_end=16, ir1_start=0, ir1_end=2) at /home/hbaastrup/bitnet/BitNet.debug/3rdparty/llama.cpp/ggml/src/ggml-cpu/ggml-cpu.c:1295
#2 0x00007ffff5e0ad40 in ggml_compute_forward_mul_mat (params=0x7ffffffee9a0, dst=0x23f0730)
at /home/hbaastrup/bitnet/BitNet.debug/3rdparty/llama.cpp/ggml/src/ggml-cpu/ggml-cpu.c:1608
...

When I take a better look at the file 3rdparty/llama.cpp/ggml/src/ggml-cpu/ggml-cpu.c, I see the type_traits_cpu array does not have an value for the index of GGML_TYPE_TL2 (42), which means; when the program try to lookup the vec_dot reference, it get a 0 (null) there will provoke a SIGSEGV when vec_dot is called.

Is there a quick fix for this?

Note
I don't use the I2_S format as llama-quantize does not support this format any longer. I get:
llama_quantize: invalid ftype 'I2_S'
when I run python3 setup_env.py --hf-repo HF1BitLLM/Llama3-8B-1.58-100B-tokens -q i2_s

Looking in the file 3rdparty/llama.cpp/tools/quantize/quantize.cpp I can see the std::vector QUANT_OPTIONS missing an index for I2_S

Reproduce with the listed setup_env.py and run_inference.py commands, then inspect ggml/src/ggml-cpu/ggml-cpu.c around ggml_compute_forward_mul_mat and the type_traits_cpu array. Also check tools/quantize/quantize.cpp and its QUANT_OPTIONS entry for I2_S. Done means the TL2 GGUF inference no longer segfaults and the reported I2_S quantization option is handled consistently.