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Update llama.cpp
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llama_cpp/llama_cpp.py

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@@ -201,25 +201,6 @@ def llama_apply_lora_from_file(
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_lib.llama_apply_lora_from_file.restype = c_int
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# Returns the KV cache that will contain the context for the
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# ongoing prediction with the model.
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def llama_get_kv_cache(ctx: llama_context_p):
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return _lib.llama_get_kv_cache(ctx)
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_lib.llama_get_kv_cache.argtypes = [llama_context_p]
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_lib.llama_get_kv_cache.restype = POINTER(c_uint8)
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# Returns the size of the KV cache
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def llama_get_kv_cache_size(ctx: llama_context_p) -> c_size_t:
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return _lib.llama_get_kv_cache_size(ctx)
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_lib.llama_get_kv_cache_size.argtypes = [llama_context_p]
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_lib.llama_get_kv_cache_size.restype = c_size_t
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# Returns the number of tokens in the KV cache
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def llama_get_kv_cache_token_count(ctx: llama_context_p) -> c_int:
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return _lib.llama_get_kv_cache_token_count(ctx)
@@ -229,17 +210,6 @@ def llama_get_kv_cache_token_count(ctx: llama_context_p) -> c_int:
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_lib.llama_get_kv_cache_token_count.restype = c_int
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# Sets the KV cache containing the current context for the model
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def llama_set_kv_cache(
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ctx: llama_context_p, kv_cache, n_size: c_size_t, n_token_count: c_int
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):
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return _lib.llama_set_kv_cache(ctx, kv_cache, n_size, n_token_count)
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_lib.llama_set_kv_cache.argtypes = [llama_context_p, POINTER(c_uint8), c_size_t, c_int]
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_lib.llama_set_kv_cache.restype = None
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# Returns the size in bytes of the state (rng, logits, embedding and kv_cache)
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def llama_get_state_size(ctx: llama_context_p) -> c_size_t:
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return _lib.llama_get_state_size(ctx)

vendor/llama.cpp

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