Update TP example

examples/inference_tp.py

@@ -10,9 +10,22 @@
 config.arch_compat_overrides()
 config.no_graphs = True
 model = ExLlamaV2(config)
-model.load_tp(progress = True)
+
+# Load the model in tensor-parallel mode. With no gpu_split specified, the model will attempt to split across
+# all visible devices according to the currently available VRAM on each. expect_cache_tokens is necessary for
+# balancing the split, in case the GPUs are of uneven sizes, or if the number of GPUs doesn't divide the number
+# of KV heads in the model
+#
+# The cache type for a TP model is always ExLlamaV2Cache_TP and should be allocated after the model. To use a
+# quantized cache, add a `base = ExLlamaV2Cache_Q6` etc. argument to the cache constructor. It's advisable
+# to also add `expect_cache_base = ExLlamaV2Cache_Q6` to load_tp() as well so the size can be correctly
+# accounted for when splitting the model.
+
+model.load_tp(progress = True, expect_cache_tokens = 16384)
 cache = ExLlamaV2Cache_TP(model, max_seq_len = 16384)
 
+# After loading the model, all other functions should work the same
+
 print("Loading tokenizer...")
 tokenizer = ExLlamaV2Tokenizer(config)