sampling: add Top-nσ sampler (#11223)

* initial sampling changes:

* completed top nsigma sampler implementation

* apply parameter to only llama-cli

* updated readme

* added tests and fixed nsigma impl

* cleaned up pr

* format

* format

* format

* removed commented tests

* cleanup pr and remove explicit floats

* added top-k sampler to improve performance

* changed sigma to float

* fixed string format to float

* Update src/llama-sampling.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update common/sampling.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update src/llama-sampling.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update src/llama-sampling.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update src/llama-sampling.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update src/llama-sampling.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* added llama_sampler_init

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

include/llama.h

@@ -1172,6 +1172,9 @@ extern "C" {
     /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335
     LLAMA_API struct llama_sampler * llama_sampler_init_xtc        (float   p, float   t,     size_t min_keep, uint32_t seed);
 
+    /// @details Top n sigma sampling as described in academic paper "Top-nσ: Not All Logits Are You Need" https://arxiv.org/pdf/2411.07641
+    LLAMA_API struct llama_sampler * llama_sampler_init_top_n_sigma(float   n);
+
     /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
     /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
     /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.