Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	examples/batched/batched.cpp
#	ggml/src/ggml-opencl/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	src/llama-context.cpp
#	tools/cli/README.md
#	tools/completion/README.md
#	tools/server/README.md

include/llama-cpp.h

@@ -21,7 +21,9 @@ struct llama_sampler_deleter {
 };
 
 struct llama_adapter_lora_deleter {
-    void operator()(llama_adapter_lora * adapter) { llama_adapter_lora_free(adapter); }
+    void operator()(llama_adapter_lora *) {
+        // llama_adapter_lora_free is deprecated
+    }
 };
 
 typedef std::unique_ptr<llama_model, llama_model_deleter> llama_model_ptr;

include/llama.h

@@ -649,7 +649,8 @@ extern "C" {
 
     // Manually free a LoRA adapter
     // NOTE: loaded adapters will be free when the associated model is deleted
-    LLAMA_API void llama_adapter_lora_free(struct llama_adapter_lora * adapter);
+    LLAMA_API DEPRECATED(void llama_adapter_lora_free(struct llama_adapter_lora * adapter),
+            "adapters are now freed together with the associated model");
 
     // Get the invocation tokens if the current lora is an alora
     LLAMA_API uint64_t            llama_adapter_get_alora_n_invocation_tokens(const struct llama_adapter_lora * adapter);
@@ -1258,7 +1259,6 @@ extern "C" {
     // [EXPERIMENTAL]
     // attach a sampler to the context
     // note: prefer initializing the context with llama_context_params.samplers when possible
-    // note: changing the samplers of a context can cause graph reallocations and degraded performance
     LLAMA_API bool llama_set_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl);
 
     // mirror of llama_sampler_i:
@@ -1398,6 +1398,33 @@ extern "C" {
                           const char ** seq_breakers,
                               size_t    num_breakers);
 
+    /// adaptive-p: select tokens near a configurable target probability over time.
+    ///
+    /// the adaptive-p sampler transforms the token probability distribution to favor tokens
+    /// that fall near a user-configurable probability target.
+    ///
+    /// internally, the sampler maintains an exponential moving average of the *ORIGINAL*
+    /// probabilities of selected tokens at each sampling step. it uses this EMA to compute an
+    /// adapted target probability at each sampling step, thus maintaining the desired target
+    /// probability over time.
+    ///
+    /// adaptive-p selects a token ID rather than just mutating candidates, so it must be last
+    /// in the sampler chain (like mirostat, dist, greedy).
+    ///
+    /// only mild truncation before this sampler is recommended. we suggest applying min-p
+    /// before adaptive-p as the only other active sampler in the chain.
+    ///
+    /// @param target select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
+    /// @param decay  EMA decay for adaptation; history ≈ 1/(1-decay) tokens (valid range 0.0 - 0.99)
+    /// @param seed   RNG seed
+    ///
+    /// ref: https://github.com/ggml-org/llama.cpp/pull/17927
+    ///
+    LLAMA_API struct llama_sampler * llama_sampler_init_adaptive_p(
+                               float   target,
+                               float   decay,
+                            uint32_t   seed);
+
     LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias(
                              int32_t   n_vocab,
                              int32_t   n_logit_bias,