diff --git a/common/common.cpp b/common/common.cpp
index a084c827a..f0626a928 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -109,7 +109,7 @@ int32_t get_num_physical_cores() {
     return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
 }
 
-#if defined(__x86_64__) && defined(__linux__)
+#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
 #include <pthread.h>
 
 static void cpuid(unsigned leaf, unsigned subleaf,
@@ -163,7 +163,7 @@ static int count_math_cpus(int cpu_count) {
  * Returns number of CPUs on system that are useful for math.
  */
 int get_math_cpu_count() {
-#if defined(__x86_64__) && defined(__linux__)
+#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
     int cpu_count = sysconf(_SC_NPROCESSORS_ONLN);
     if (cpu_count < 1) {
         return get_num_physical_cores();
@@ -2329,10 +2329,10 @@ std::vector<llama_token> llama_tokenize(
 
 std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
     std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), true);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), true);
         GGML_ASSERT(check == -n_tokens);
     } else {
         result.resize(n_tokens);
diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 358dba8ed..4fd916cba 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -1301,15 +1301,23 @@ class LlamaModel(Model):
         try:
             self. _set_vocab_sentencepiece()
         except FileNotFoundError:
-            self._set_vocab_llama_hf()
+            try:
+                self._set_vocab_llama_hf()
+            except (FileNotFoundError, TypeError):
+                # Llama 3
+                self._set_vocab_gpt2()
 
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
-                                          special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
-        special_vocab._set_special_token("prefix", 32007)
-        special_vocab._set_special_token("suffix", 32008)
-        special_vocab._set_special_token("middle", 32009)
-        special_vocab._set_special_token("eot",    32010)
-        special_vocab.add_to_gguf(self.gguf_writer)
+        # Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256)
+        if self.hparams.get("vocab_size", 32000) == 32016:
+            special_vocab = gguf.SpecialVocab(
+                self.dir_model, load_merges=False,
+                special_token_types = ['prefix', 'suffix', 'middle', 'eot']
+            )
+            special_vocab._set_special_token("prefix", 32007)
+            special_vocab._set_special_token("suffix", 32008)
+            special_vocab._set_special_token("middle", 32009)
+            special_vocab._set_special_token("eot",    32010)
+            special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
@@ -2194,6 +2202,8 @@ class InternLM2Model(Model):
         old_eos = special_vocab.special_token_ids["eos"]
         if "chat" in os.path.basename(self.dir_model.absolute()):
             # For the chat model, we replace the eos with '<|im_end|>'.
+            # TODO: this is a hack, should be fixed
+            #       https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048
             special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
             print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
 in chat mode so that the conversation can end normally.")
@@ -2429,12 +2439,15 @@ class GemmaModel(Model):
 
     def set_vocab(self):
         self._set_vocab_sentencepiece()
+
+        # TODO: these special tokens should be exported only for the CodeGemma family
         special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
-                                          special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
+                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
         special_vocab._set_special_token("prefix", 67)
         special_vocab._set_special_token("suffix", 69)
         special_vocab._set_special_token("middle", 68)
-        special_vocab._set_special_token("eot",    70)
+        special_vocab._set_special_token("fsep",   70)
+        special_vocab._set_special_token("eot",    107)
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
@@ -2523,28 +2536,34 @@ class MambaModel(Model):
 
             field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
             self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]))
+
             field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
             self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
+
             field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
             self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
+
             field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
             self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
+
             field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
             self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
+
             field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
             self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
+
             field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
             self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
 
     def set_gguf_parameters(self):
-        d_model = self.find_hparam(["hidden_size", "d_model"])
-        d_conv  = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
+        d_model = self.find_hparam(["hidden_size",       "d_model"])
+        d_conv  = self.find_hparam(["conv_kernel",       "d_conv"],  optional=True) or 4
         d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
-        d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
+        d_state = self.find_hparam(["state_size",        "d_state"], optional=True) or 16
         # ceiling division
         # ref: https://stackoverflow.com/a/17511341/22827863
         # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
-        dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
+        dt_rank      = self.find_hparam(["time_step_rank",     "dt_rank"],      optional=True) or -(d_model // -16)
         rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
 
         # Fail early for models which don't have a block expansion factor of 2
diff --git a/convert.py b/convert.py
index 24df0a4d8..1c700cf6a 100755
--- a/convert.py
+++ b/convert.py
@@ -525,7 +525,14 @@ class LlamaHfVocab(Vocab):
 
         # pre-check so we know if we need transformers
         tokenizer_model: dict[str, Any] = tokenizer_json['model']
-        if (
+        is_llama3 = (
+            tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
+            and not tokenizer_model.get('byte_fallback', True)
+        )
+        if is_llama3:
+            raise TypeError('Llama 3 must be converted with BpeVocab')
+
+        if not is_llama3 and (
             tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
             or tokenizer_json['decoder']['type'] != 'Sequence'
         ):
diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift
index d75c503d5..dbbd06da5 100644
--- a/examples/batched.swift/Sources/main.swift
+++ b/examples/batched.swift/Sources/main.swift
@@ -153,7 +153,7 @@ while n_cur <= n_len {
         // const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
         // is it an end of stream? -> mark the stream as finished
-        if new_token_id == llama_token_eos(model) || n_cur == n_len {
+        if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
             i_batch[i] = -1
             // print("")
             if n_parallel > 1 {
@@ -229,7 +229,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
 
 private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
     var result = [CChar](repeating: 0, count: 8)
-    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
+    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), false)
     if nTokens < 0 {
         let actualTokensCount = -Int(nTokens)
         result = .init(repeating: 0, count: actualTokensCount)
@@ -237,7 +237,8 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
             model,
             token,
             &result,
-            Int32(result.count)
+            Int32(result.count),
+            false
         )
         assert(check == actualTokensCount)
     } else {
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 7aaf63ceb..be30d20bf 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -191,8 +191,8 @@ int main(int argc, char ** argv) {
 
             //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
-            // is it an end of stream? -> mark the stream as finished
-            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+            // is it an end of generation? -> mark the stream as finished
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
                 i_batch[i] = -1;
                 LOG_TEE("\n");
                 if (n_parallel > 1) {
diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp
index 866c6d7a6..3d34378a5 100644
--- a/examples/beam-search/beam-search.cpp
+++ b/examples/beam-search/beam-search.cpp
@@ -47,7 +47,7 @@ struct beam_search_callback_data {
 // In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
 // For example, eob can be flagged due to maximum token length, stop words, etc.
 static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
-    return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx));
+    return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]);
 }
 
 // Function matching type llama_beam_search_callback_fn_t.
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index 5848fc628..13a8bbe1c 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -587,7 +587,7 @@ int main(int argc, char ** argv) {
 
             // deal with eot token in infill mode
             if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
-                if(is_interacting && !params.interactive_first) {
+                if (is_interacting && !params.interactive_first) {
                     // print an eot token
                     printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
                 }
@@ -652,8 +652,8 @@ int main(int argc, char ** argv) {
                 // LOG_TEE("took new input\n");
                 is_interacting = false;
             }
-            // deal with end of text token in interactive mode
-            else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
+            // deal with end of generation tokens in interactive mode
+            else if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
                 LOG("found EOS token\n");
 
                 if (params.interactive) {
@@ -732,8 +732,8 @@ int main(int argc, char ** argv) {
             }
         }
 
-        // end of text token
-        if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) {
+        // end of generation
+        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) {
             break;
         }
 
diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/app/src/main/cpp/llama-android.cpp
index ce8ab3b70..4af9de303 100644
--- a/examples/llama.android/app/src/main/cpp/llama-android.cpp
+++ b/examples/llama.android/app/src/main/cpp/llama-android.cpp
@@ -408,7 +408,7 @@ Java_com_example_llama_Llm_completion_1loop(
     const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);
 
     const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
-    if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+    if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
         return env->NewStringUTF("");
     }
 
diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
index c249291ae..737f882fb 100644
--- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
+++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
@@ -158,7 +158,7 @@ actor LlamaContext {
             new_token_id = llama_sample_token_greedy(context, &candidates_p)
         }
 
-        if new_token_id == llama_token_eos(model) || n_cur == n_len {
+        if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
             print("\n")
             let new_token_str = String(cString: temporary_invalid_cchars + [0])
             temporary_invalid_cchars.removeAll()
@@ -322,7 +322,7 @@ actor LlamaContext {
         defer {
             result.deallocate()
         }
-        let nTokens = llama_token_to_piece(model, token, result, 8)
+        let nTokens = llama_token_to_piece(model, token, result, 8, false)
 
         if nTokens < 0 {
             let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
@@ -330,7 +330,7 @@ actor LlamaContext {
             defer {
                 newResult.deallocate()
             }
-            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
+            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false)
             let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
             return Array(bufferPointer)
         } else {
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 5954bf6cd..e431c7f70 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -3,6 +3,7 @@
 // I'll gradually clean and extend it
 // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
 #include "clip.h"
+#include "log.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
@@ -23,7 +24,6 @@
 #include <cstdlib>
 #include <cstring>
 #include <fstream>
-#include <iostream>
 #include <map>
 #include <regex>
 #include <stdexcept>
@@ -145,7 +145,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
 static int get_key_idx(const gguf_context * ctx, const char * key) {
     int i = gguf_find_key(ctx, key);
     if (i == -1) {
-        fprintf(stderr, "key %s not found in file\n", key);
+        LOG_TEE("key %s not found in file\n", key);
         throw std::runtime_error(format("Missing required key: %s", key));
     }
 
@@ -247,7 +247,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
 
 static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") {
     size_t tensor_size = ggml_nbytes(tensor);
-    printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
+    LOG_TEE("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
             prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
             tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
 }
@@ -265,7 +265,7 @@ static projector_type clip_projector_type_from_string(const std::string & name)
 static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
     std::ofstream file(filename, std::ios::binary);
     if (!file.is_open()) {
-        std::cerr << "Failed to open file for writing: " << filename << std::endl;
+        LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
         return;
     }
 
@@ -284,7 +284,7 @@ static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::s
 static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
     std::ofstream file(filename, std::ios::binary);
     if (!file.is_open()) {
-        std::cerr << "Failed to open file for writing: " << filename << std::endl;
+        LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
         return;
     }
 
@@ -515,7 +515,7 @@ struct clip_ctx {
 
 static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return nullptr;
     }
 
@@ -879,21 +879,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         const int idx_name = gguf_find_key(ctx, KEY_NAME);
         if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug
             const std::string name = gguf_get_val_str(ctx, idx_name);
-            printf("%s: model name:   %s\n", __func__, name.c_str());
+            LOG_TEE("%s: model name:   %s\n", __func__, name.c_str());
         }
-        printf("%s: description:  %s\n", __func__, description.c_str());
-        printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
-        printf("%s: alignment:    %zu\n", __func__, gguf_get_alignment(ctx));
-        printf("%s: n_tensors:    %d\n", __func__, n_tensors);
-        printf("%s: n_kv:         %d\n", __func__, n_kv);
-        printf("%s: ftype:        %s\n", __func__, ftype_str.c_str());
-        printf("\n");
+        LOG_TEE("%s: description:  %s\n", __func__, description.c_str());
+        LOG_TEE("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
+        LOG_TEE("%s: alignment:    %zu\n", __func__, gguf_get_alignment(ctx));
+        LOG_TEE("%s: n_tensors:    %d\n", __func__, n_tensors);
+        LOG_TEE("%s: n_kv:         %d\n", __func__, n_kv);
+        LOG_TEE("%s: ftype:        %s\n", __func__, ftype_str.c_str());
+        LOG_TEE("\n");
     }
     const int n_tensors = gguf_get_n_tensors(ctx);
 
     // kv
     const int n_kv = gguf_get_n_kv(ctx);
-    printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
+    LOG_TEE("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
         __func__, n_kv, n_tensors, fname);
     {
         std::map<enum ggml_type, uint32_t> n_type;
@@ -904,7 +904,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             n_type[type]++;
         }
 
-        printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
+        LOG_TEE("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
         for (int i = 0; i < n_kv; i++) {
             const char * name           = gguf_get_key(ctx, i);
             const enum gguf_type type   = gguf_get_kv_type(ctx, i);
@@ -920,7 +920,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             }
             replace_all(value, "\n", "\\n");
 
-            printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
+            LOG_TEE("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
         }
 
         // print type counts
@@ -929,7 +929,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
                 continue;
             }
 
-            printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
+            LOG_TEE("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
         }
     }
 
@@ -944,7 +944,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             size_t tensor_size = ggml_nbytes(cur);
             model_size += tensor_size;
             if (verbosity >= 3) {
-                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
+                LOG_TEE("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
                        __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
             }
         }
@@ -971,18 +971,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
 
 #ifdef GGML_USE_CUDA
     new_clip->backend = ggml_backend_cuda_init(0);
-    printf("%s: CLIP using CUDA backend\n", __func__);
+    LOG_TEE("%s: CLIP using CUDA backend\n", __func__);
 #endif
 
 #ifdef GGML_USE_METAL
     new_clip->backend = ggml_backend_metal_init();
-    printf("%s: CLIP using Metal backend\n", __func__);
+    LOG_TEE("%s: CLIP using Metal backend\n", __func__);
 #endif
 
 
     if (!new_clip->backend) {
         new_clip->backend = ggml_backend_cpu_init();
-        printf("%s: CLIP using CPU backend\n", __func__);
+        LOG_TEE("%s: CLIP using CPU backend\n", __func__);
     }
 
     // model size and capabilities
@@ -1006,15 +1006,15 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
 
         if (verbosity >= 1) {
-            printf("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
-            printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
-            printf("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
-            printf("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
-            printf("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
+            LOG_TEE("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
+            LOG_TEE("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
+            LOG_TEE("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
+            LOG_TEE("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
+            LOG_TEE("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
         }
     }
 
-    printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
+    LOG_TEE("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
 
     // load tensors
     {
@@ -1027,7 +1027,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
 
         new_clip->ctx_data = ggml_init(params);
         if (!new_clip->ctx_data) {
-            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+            LOG_TEE("%s: ggml_init() failed\n", __func__);
             clip_free(new_clip);
             gguf_free(ctx);
             return nullptr;
@@ -1035,7 +1035,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
 
         auto fin = std::ifstream(fname, std::ios::binary);
         if (!fin) {
-            printf("cannot open model file for loading tensors\n");
+            LOG_TEE("cannot open model file for loading tensors\n");
             clip_free(new_clip);
             gguf_free(ctx);
             return nullptr;
@@ -1057,7 +1057,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
             fin.seekg(offset, std::ios::beg);
             if (!fin) {
-                printf("%s: failed to seek for tensor %s\n", __func__, name);
+                LOG_TEE("%s: failed to seek for tensor %s\n", __func__, name);
                 clip_free(new_clip);
                 gguf_free(ctx);
                 return nullptr;
@@ -1128,23 +1128,23 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         }
 
         if (verbosity >= 2) {
-            printf("\n%s: vision model hparams\n", __func__);
-            printf("image_size         %d\n", hparams.image_size);
-            printf("patch_size         %d\n", hparams.patch_size);
-            printf("v_hidden_size      %d\n", hparams.hidden_size);
-            printf("v_n_intermediate   %d\n", hparams.n_intermediate);
-            printf("v_projection_dim   %d\n", hparams.projection_dim);
-            printf("v_n_head           %d\n", hparams.n_head);
-            printf("v_n_layer          %d\n", hparams.n_layer);
-            printf("v_eps              %f\n", hparams.eps);
-            printf("v_image_mean       %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
-            printf("v_image_std        %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
-            printf("v_image_grid_pinpoints: ");
+            LOG_TEE("\n%s: vision model hparams\n", __func__);
+            LOG_TEE("image_size         %d\n", hparams.image_size);
+            LOG_TEE("patch_size         %d\n", hparams.patch_size);
+            LOG_TEE("v_hidden_size      %d\n", hparams.hidden_size);
+            LOG_TEE("v_n_intermediate   %d\n", hparams.n_intermediate);
+            LOG_TEE("v_projection_dim   %d\n", hparams.projection_dim);
+            LOG_TEE("v_n_head           %d\n", hparams.n_head);
+            LOG_TEE("v_n_layer          %d\n", hparams.n_layer);
+            LOG_TEE("v_eps              %f\n", hparams.eps);
+            LOG_TEE("v_image_mean       %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
+            LOG_TEE("v_image_std        %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
+            LOG_TEE("v_image_grid_pinpoints: ");
             for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
-                printf("%d ", hparams.image_grid_pinpoints[i]);
+                LOG_TEE("%d ", hparams.image_grid_pinpoints[i]);
             }
-            printf("\n");
-            printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
+            LOG_TEE("\n");
+            LOG_TEE("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
 
         }
 
@@ -1155,7 +1155,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.pre_ln_w            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
             vision_model.pre_ln_b            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
         } catch(const std::exception& e) {
-            fprintf(stderr, "%s: failed to load vision model tensors\n", __func__);
+            LOG_TEE("%s: failed to load vision model tensors\n", __func__);
         }
 
         // LLaVA projection
@@ -1184,7 +1184,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             } catch (std::runtime_error & e) {  }
             try {
                 vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
-                // fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__);
+                // LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
             } catch (std::runtime_error & e) {  }
         } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
             // MobileVLM projection
@@ -1264,7 +1264,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
         ggml_gallocr_reserve(new_clip->compute_alloc, gf);
         size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
-        printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
+        LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
     }
 
     return new_clip;
@@ -1304,7 +1304,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
     int nx, ny, nc;
     auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
     if (!data) {
-        fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
+        LOG_TEE("%s: failed to load image '%s'\n", __func__, fname);
         return false;
     }
     build_clip_img_from_data(data, nx, ny, img);
@@ -1316,7 +1316,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
     int nx, ny, nc;
     auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
     if (!data) {
-        fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
+        LOG_TEE("%s: failed to decode image bytes\n", __func__);
         return false;
     }
     build_clip_img_from_data(data, nx, ny, img);
@@ -1506,7 +1506,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int> & or
         int downscaled_height = static_cast<int>(original_height * scale);
         int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
         int wasted_resolution = (width * height) - effective_resolution;
-        // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
+        // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
         if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
             max_effective_resolution = effective_resolution;
             min_wasted_resolution = wasted_resolution;
@@ -1545,7 +1545,7 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
 bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
     bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return false;
     }
     auto & params = ctx->vision_model.hparams;
@@ -1622,7 +1622,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
             }
 
             for (size_t i = 0; i < patches.size(); i++) {
-                // printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
+                // LOG_TEE("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
                 clip_image_u8_free(patches[i]);
             }
 
@@ -1765,7 +1765,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {
 
 bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return false;
     }
 
@@ -1777,7 +1777,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3
 
 bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
     if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
         return false;
     }
 
@@ -1939,7 +1939,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             new_type = type;
             if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) {
                 new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type
-                // fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
+                // LOG_TEE("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
             }
             const size_t n_elms = ggml_nelements(cur);
             float * f32_data;
@@ -1958,7 +1958,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
                 f32_data = (float *)conv_buf.data();
                 break;
             default:
-                printf("Please use an input file in f32 or f16\n");
+                LOG_TEE("Please use an input file in f32 or f16\n");
                 gguf_free(ctx_out);
                 return false;
             }
@@ -1985,7 +1985,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             fout.put(0);
         }
 
-        printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
+        LOG_TEE("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
                orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
     }
 
@@ -2001,8 +2001,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
     gguf_free(ctx_out);
 
     {
-        printf("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
-        printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
+        LOG_TEE("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
+        LOG_TEE("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
     }
 
     return true;
diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp
index 75948806e..a44c6cd76 100644
--- a/examples/llava/llava-cli.cpp
+++ b/examples/llava/llava-cli.cpp
@@ -1,4 +1,5 @@
 #include "ggml.h"
+#include "log.h"
 #include "common.h"
 #include "clip.h"
 #include "llava.h"
@@ -18,7 +19,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
             n_eval = n_batch;
         }
         if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
-            fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
+            LOG_TEE("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
             return false;
         }
         *n_past += n_eval;
@@ -45,7 +46,7 @@ static const char * sample(struct llama_sampling_context * ctx_sampling,
     const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL);
     llama_sampling_accept(ctx_sampling, ctx_llama, id, true);
     static std::string ret;
-    if (id == llama_token_eos(llama_get_model(ctx_llama))) {
+    if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
         ret = "</s>";
     } else {
         ret = llama_token_to_piece(ctx_llama, id);
@@ -73,7 +74,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
     size_t img_base64_str_start, img_base64_str_end;
     find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
     if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
-        fprintf(stderr, "%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
+        LOG_TEE("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
         return NULL;
     }
 
@@ -87,7 +88,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
 
     auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
     if (!embed) {
-        fprintf(stderr, "%s: could not load image from base64 string.\n", __func__);
+        LOG_TEE("%s: could not load image from base64 string.\n", __func__);
         return NULL;
     }
 
@@ -112,8 +113,8 @@ struct llava_context {
 };
 
 static void show_additional_info(int /*argc*/, char ** argv) {
-    fprintf(stderr, "\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    fprintf(stderr, "  note: a lower temperature value like 0.1 is recommended for better quality.\n");
+    LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG_TEE("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
 }
 
 static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) {
@@ -123,18 +124,18 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para
     auto prompt = params->prompt;
     if (prompt_contains_image(prompt)) {
         if (!params->image.empty()) {
-            fprintf(stderr, "using base64 encoded image instead of command line image path\n");
+            LOG_TEE("using base64 encoded image instead of command line image path\n");
         }
         embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt);
         if (!embed) {
-            fprintf(stderr, "%s: can't load image from prompt\n", __func__);
+            LOG_TEE("%s: can't load image from prompt\n", __func__);
             return NULL;
         }
         params->prompt = remove_image_from_prompt(prompt);
     } else {
         embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str());
         if (!embed) {
-            fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str());
+            LOG_TEE("%s: is %s really an image file?\n", __func__, params->image.c_str());
             return NULL;
         }
     }
@@ -153,18 +154,18 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
         // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
         system_prompt = prompt.substr(0, image_pos);
         user_prompt = prompt.substr(image_pos + std::string("<image>").length());
-        printf("system_prompt: %s\n", system_prompt.c_str());
+        LOG_TEE("system_prompt: %s\n", system_prompt.c_str());
         if (params->verbose_prompt) {
             auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
             for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
             }
         }
-        printf("user_prompt: %s\n", user_prompt.c_str());
+        LOG_TEE("user_prompt: %s\n", user_prompt.c_str());
         if (params->verbose_prompt) {
             auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
             for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
             }
         }
     } else {
@@ -174,7 +175,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
         if (params->verbose_prompt) {
             auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
             for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
             }
         }
     }
@@ -185,7 +186,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
 
     // generate the response
 
-    fprintf(stderr, "\n");
+    LOG_TEE("\n");
 
     struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
     std::string response = "";
@@ -224,7 +225,7 @@ static struct llava_context * llava_init(gpt_params * params) {
 
     llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
     if (model == NULL) {
-        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
+        LOG_TEE("%s: error: unable to load model\n" , __func__);
         return NULL;
     }
 
@@ -234,7 +235,7 @@ static struct llava_context * llava_init(gpt_params * params) {
     llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
 
     if (ctx_llama == NULL) {
-        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        LOG_TEE("%s: error: failed to create the llama_context\n" , __func__);
         return NULL;
     }
 
@@ -257,6 +258,12 @@ static void llava_free(struct llava_context * ctx_llava) {
     llama_backend_free();
 }
 
+static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    LOG_TEE("%s", text);
+}
+
 int main(int argc, char ** argv) {
     ggml_time_init();
 
@@ -266,6 +273,14 @@ int main(int argc, char ** argv) {
         show_additional_info(argc, argv);
         return 1;
     }
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("llava", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+    llama_log_set(llama_log_callback_logTee, nullptr);
+#endif // LOG_DISABLE_LOGS
+
     if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
         gpt_print_usage(argc, argv, params);
         show_additional_info(argc, argv);
@@ -274,7 +289,7 @@ int main(int argc, char ** argv) {
 
     auto ctx_llava = llava_init(&params);
     if (ctx_llava == NULL) {
-        fprintf(stderr, "%s: error: failed to init llava\n", __func__);
+        LOG_TEE("%s: error: failed to init llava\n", __func__);
         return 1;
     }
 
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 29764757a..9a990bb18 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -54,7 +54,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int>& ori
         int downscaled_height = static_cast<int>(original_height * scale);
         int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
         int wasted_resolution = (width * height) - effective_resolution;
-        // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
+        // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
         if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
             max_effective_resolution = effective_resolution;
             min_wasted_resolution = wasted_resolution;
@@ -154,13 +154,13 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
     model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
     if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) {
         if (newline_tmp->buffer == NULL) {
-            printf("newline_tmp tensor buffer is NULL\n");
+            LOG_TEE("newline_tmp tensor buffer is NULL\n");
         }
         ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp));
     } else {
         model.newline->data = newline_tmp->data;
         if (model.newline->data == NULL) {
-            printf("newline_tmp tensor data is NULL\n");
+            LOG_TEE("newline_tmp tensor data is NULL\n");
         }
     }
 
@@ -224,7 +224,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
     img_res_v.size = 0;
     img_res_v.data = nullptr;
     if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
-        fprintf(stderr, "%s: unable to preprocess image\n", __func__);
+        LOG_TEE("%s: unable to preprocess image\n", __func__);
         delete[] img_res_v.data;
         return false;
     }
@@ -239,7 +239,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
         delete[] img_res_v.data;
         if (!encoded) {
-            fprintf(stderr, "Unable to encode image\n");
+            LOG_TEE("Unable to encode image\n");
 
             return false;
         }
@@ -252,12 +252,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
             image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
             const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
             if (!encoded) {
-                fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
+                LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
                 return false;
             }
         }
         const int64_t t_img_enc_batch_us = ggml_time_us();
-        printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
+        LOG_TEE("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
 
         const int32_t * image_grid = clip_image_grid(ctx_clip);
 
@@ -290,12 +290,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         // clip_image_save_to_bmp(*tmp, "image_feature.bmp");
     }
 
-    printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
+    LOG_TEE("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
 
     const int64_t t_img_enc_end_us = ggml_time_us();
     float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
 
-    printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
+    LOG_TEE("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
 
     return true;
 }
@@ -305,7 +305,7 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
     int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
     auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
     if (n_image_embd != n_llama_embd) {
-        printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
+        LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
         return false;
     }
     return true;
@@ -314,13 +314,13 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
 bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
     float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
     if (!image_embd) {
-        fprintf(stderr, "Unable to allocate memory for image embeddings\n");
+        LOG_TEE("Unable to allocate memory for image embeddings\n");
         return false;
     }
 
     int n_img_pos;
     if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
-        fprintf(stderr, "%s: cannot encode image, aborting\n", __func__);
+        LOG_TEE("%s: cannot encode image, aborting\n", __func__);
         free(image_embd);
         return false;
     }
@@ -340,7 +340,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
         }
         llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
         if (llama_decode(ctx_llama, batch)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
+            LOG_TEE("%s : failed to eval\n", __func__);
             return false;
         }
         *n_past += n_eval;
@@ -352,7 +352,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
     clip_image_u8 * img = clip_image_u8_init();
     if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
         clip_image_u8_free(img);
-        fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);
+        LOG_TEE("%s: can't load image from bytes, is it a valid image?", __func__);
         return NULL;
     }
 
@@ -361,7 +361,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
     bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
     if (!image_embed_result) {
         clip_image_u8_free(img);
-        fprintf(stderr, "%s: coulnd't embed the image\n", __func__);
+        LOG_TEE("%s: coulnd't embed the image\n", __func__);
         return NULL;
     }
 
@@ -375,7 +375,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
 static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
     auto file = fopen(path, "rb");
     if (file == NULL) {
-        fprintf(stderr, "%s: can't read file %s\n", __func__, path);
+        LOG_TEE("%s: can't read file %s\n", __func__, path);
         return false;
     }
 
@@ -385,7 +385,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long
 
     auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
     if (buffer == NULL) {
-        fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
+        LOG_TEE("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
         perror("Memory allocation error");
         fclose(file);
         return false;
@@ -410,7 +410,7 @@ struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx
     long image_bytes_length;
     auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
     if (!loaded) {
-        fprintf(stderr, "%s: failed to load %s\n", __func__, image_path);
+        LOG_TEE("%s: failed to load %s\n", __func__, image_path);
         return NULL;
     }
 
diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp
index 5af6a8ab6..9c3540b20 100644
--- a/examples/lookahead/lookahead.cpp
+++ b/examples/lookahead/lookahead.cpp
@@ -299,7 +299,7 @@ int main(int argc, char ** argv) {
                 }
                 fflush(stdout);
 
-                if (id == llama_token_eos(model)) {
+                if (llama_token_is_eog(model, id)) {
                     has_eos = true;
                 }
 
diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp
index 65ed408a2..9526e898f 100644
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -141,7 +141,7 @@ int main(int argc, char ** argv){
                 printf("%s", token_str.c_str());
             }
 
-            if (id == llama_token_eos(model)) {
+            if (llama_token_is_eog(model, id)) {
                 has_eos = true;
             }
 
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 7eef62eb9..8e9126d7a 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -796,8 +796,8 @@ int main(int argc, char ** argv) {
                 }
             }
 
-            // deal with end of text token in interactive mode
-            if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
+            // deal with end of generation tokens in interactive mode
+            if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
                 LOG("found EOS token\n");
 
                 if (params.interactive) {
@@ -921,8 +921,8 @@ int main(int argc, char ** argv) {
             }
         }
 
-        // end of text token
-        if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive || params.chatml)) {
+        // end of generation
+        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) {
             LOG_TEE(" [end of text]\n");
             break;
         }
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index c7a290371..ed6b66036 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -361,7 +361,7 @@ int main(int argc, char ** argv) {
                 //        client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
 
                 if (client.n_decoded > 2 &&
-                        (id == llama_token_eos(model) ||
+                        (llama_token_is_eog(model, id) ||
                          (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
                          client.response.find("User:") != std::string::npos ||
                          client.response.find('\n') != std::string::npos)) {
diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp
index 2cbc9e1fa..f2ef9ca10 100644
--- a/examples/passkey/passkey.cpp
+++ b/examples/passkey/passkey.cpp
@@ -252,8 +252,8 @@ int main(int argc, char ** argv) {
             // sample the most likely token
             const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
-            // is it an end of stream?
-            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+            // is it an end of generation?
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
                 LOG_TEE("\n");
 
                 break;
diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt
index 61f584174..4b89c5302 100644
--- a/examples/server/CMakeLists.txt
+++ b/examples/server/CMakeLists.txt
@@ -1,12 +1,29 @@
 set(TARGET server)
 option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
 option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
-include_directories(${CMAKE_CURRENT_SOURCE_DIR})
-add_executable(${TARGET}
+include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
+set(TARGET_SRCS
     server.cpp
     utils.hpp
     httplib.h
 )
+set(PUBLIC_ASSETS
+    index.html
+    index.js
+    completion.js
+    json-schema-to-grammar.mjs
+)
+foreach(asset ${PUBLIC_ASSETS})
+    set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
+    set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
+    list(APPEND TARGET_SRCS ${output})
+    add_custom_command(
+        DEPENDS "${input}"
+        OUTPUT "${output}"
+        COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
+    )
+endforeach()
+add_executable(${TARGET} ${TARGET_SRCS})
 install(TARGETS ${TARGET} RUNTIME)
 target_compile_definitions(${TARGET} PRIVATE
     SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp
deleted file mode 100644
index 9a7f3986d..000000000
--- a/examples/server/completion.js.hpp
+++ /dev/null
@@ -1,496 +0,0 @@
-unsigned char completion_js[] = {
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-unsigned int completion_js_len = 5909;
diff --git a/examples/server/deps.sh b/examples/server/deps.sh
index ea23e6450..d28378901 100755
--- a/examples/server/deps.sh
+++ b/examples/server/deps.sh
@@ -8,13 +8,3 @@ PUBLIC=$DIR/public
 echo "download js bundle files"
 curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
 echo >> $PUBLIC/index.js # add newline
-
-FILES=$(ls $PUBLIC)
-
-cd $PUBLIC
-for FILE in $FILES; do
-  echo "generate $FILE.hpp"
-
-  # use simple flag for old version of xxd
-  xxd -i $FILE > $DIR/$FILE.hpp
-done
diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
deleted file mode 100644
index 122d3ff99..000000000
--- a/examples/server/index.html.hpp
+++ /dev/null
@@ -1,2883 +0,0 @@
-unsigned char index_html[] = {
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-unsigned int index_html_len = 34552;
diff --git a/examples/server/index.js.hpp b/examples/server/index.js.hpp
deleted file mode 100644
index 08b26550d..000000000
--- a/examples/server/index.js.hpp
+++ /dev/null
@@ -1,1911 +0,0 @@
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-unsigned int index_js_len = 22886;
diff --git a/examples/server/json-schema-to-grammar.mjs.hpp b/examples/server/json-schema-to-grammar.mjs.hpp
deleted file mode 100644
index da5035bf8..000000000
--- a/examples/server/json-schema-to-grammar.mjs.hpp
+++ /dev/null
@@ -1,1826 +0,0 @@
-unsigned char json_schema_to_grammar_mjs[] = {
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-  0x7d, 0x0a
-};
-unsigned int json_schema_to_grammar_mjs_len = 21866;
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 44e7876ee..bf957d48d 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1202,7 +1202,7 @@ struct server_context {
             });
         }
 
-        if (result.tok == llama_token_eos(model)) {
+        if (llama_token_is_eog(model, result.tok)) {
             slot.stopped_eos    = true;
             slot.has_next_token = false;
 
diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md
index feb2b1d6c..83c0208f3 100644
--- a/examples/server/tests/README.md
+++ b/examples/server/tests/README.md
@@ -29,7 +29,7 @@ To mitigate it, you can increase values in `n_predict`, `kv_size`.
 cd ../../..
 mkdir build
 cd build
-cmake ../
+cmake -DLLAMA_CURL=ON ../
 cmake --build . --target server
 ```
 
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index a8d43ac63..1a2212502 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -381,10 +381,6 @@ static json oaicompat_completion_params_parse(
     } else {
         llama_params["stop"] = json_value(body, "stop", json::array());
     }
-    // Some chat templates don't use EOS token to stop generation
-    // We must add their end sequences to list of stop words
-    llama_params["stop"].push_back("<|im_end|>"); // chatml
-    llama_params["stop"].push_back("<end_of_turn>"); // gemma
 
     // Handle "response_format" field
     if (body.contains("response_format")) {
diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp
index 39e2d8ea4..b0f8e0fdc 100644
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -133,8 +133,8 @@ int main(int argc, char ** argv) {
             // sample the most likely token
             const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
-            // is it an end of stream?
-            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+            // is it an end of generation?
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
                 LOG_TEE("\n");
 
                 break;
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 8107874a9..4ddb529b8 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -362,7 +362,7 @@ int main(int argc, char ** argv) {
                     }
                 }
 
-                if (token_id == llama_token_eos(model_tgt)) {
+                if (llama_token_is_eog(model_tgt, token_id)) {
                     has_eos = true;
                 }
                 ++n_predict;
diff --git a/expose.h b/expose.h
index dc0430099..35d9d7336 100644
--- a/expose.h
+++ b/expose.h
@@ -81,7 +81,8 @@ struct generation_inputs
     const float mirostat_tau;
     const samplers sampler_order[KCPP_SAMPLER_MAX];
     const int sampler_len;
-    const bool unban_tokens_rt;
+    const bool allow_eos_token;
+    const bool render_special;
     const char * stop_sequence[stop_token_max];
     const bool stream_sse;
     const char * grammar;
diff --git a/ggml-backend.c b/ggml-backend.c
index 402d86ef3..189b5c140 100644
--- a/ggml-backend.c
+++ b/ggml-backend.c
@@ -822,7 +822,11 @@ GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t
 GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
     switch (op->op) {
         case GGML_OP_CPY:
-            return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && op->type != GGML_TYPE_IQ1_S; // missing type_traits.from_float
+            return
+                op->type != GGML_TYPE_IQ2_XXS &&
+                op->type != GGML_TYPE_IQ2_XS  &&
+                op->type != GGML_TYPE_IQ1_S   &&
+                op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
         case GGML_OP_MUL_MAT:
             return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
         default:
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index ba24065a8..06cb26a7d 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -872,6 +872,7 @@ GGML_QUANT_SIZES = {
     GGMLQuantizationType.I32:     (1, 4),
     GGMLQuantizationType.I64:     (1, 8),
     GGMLQuantizationType.F64:     (1, 8),
+    GGMLQuantizationType.IQ1_M:   (256, QK_K // 8 + QK_K // 16  + QK_K // 32),
 }
 
 
diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index ea9ecca3f..1712ae114 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -139,7 +139,7 @@ inline bool LogitsDuplicated(std::vector<float> & arr1, std::vector<float> & arr
 }
 
 
-static std::string FileFormatTokenizeID(int id, FileFormat file_format)
+static std::string FileFormatTokenizeID(int id, FileFormat file_format, bool return_special = false)
 {
     if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2)
     {
@@ -151,7 +151,7 @@ static std::string FileFormatTokenizeID(int id, FileFormat file_format)
     }
     else if(file_format == FileFormat::GGUF_GENERIC)
     {
-        return std::string(llama_token_to_str(llama_ctx_v4, id));
+        return std::string(llama_token_to_piece(llama_ctx_v4, id, return_special));
     }
     else
     {
@@ -285,7 +285,7 @@ static std::string get_tok_vec_str(std::vector<int> &embd)
     std::string tmp = "";
     for (auto id : embd)
     {
-        tmp += "'" + FileFormatTokenizeID(id, file_format) + " (" + std::to_string(id) + ")', ";
+        tmp += "'" + FileFormatTokenizeID(id, file_format, true) + " (" + std::to_string(id) + ")', ";
     }
     ::utreplace(tmp, "\n", "\\n");
     return tmp;
@@ -604,7 +604,7 @@ static void grammar_accept_token(FileFormat file_format, int32_t n_vocab, struct
         }
         GGML_ASSERT(false);
     }
-    const std::string piece = FileFormatTokenizeID(token,file_format); //llama_token_to_str(ctx, token);
+    const std::string piece = FileFormatTokenizeID(token,file_format);
 
     // Note terminating 0 in decoded string
     const auto   decoded     = decode_utf8(piece.c_str(), grammar->partial_utf8);
@@ -1984,7 +1984,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
         printf("\n[First Run] Banning %zu token sequences...",banned_tokens.size());
         for(int v=0;v<n_vocab;++v)
         {
-            std::string word = FileFormatTokenizeID(v,file_format);
+            std::string word = FileFormatTokenizeID(v,file_format, true);
             for(int i=0;i<banned_tokens.size();++i)
             {
                 if (word.find(banned_tokens[i]) != std::string::npos)
@@ -2171,7 +2171,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
                 lowestLogit = LowestLogit(logits);
             }
 
-            if (!inputs.unban_tokens_rt)
+            if (!inputs.allow_eos_token)
             {
                 // set the logit of the eos token to very low to avoid sampling it
                 logitsPtr[eosID] = lowestLogit;
@@ -2204,7 +2204,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
 
             for (auto id : embd)
             {
-                std::string tokenizedstr = FileFormatTokenizeID(id, file_format);
+                std::string tokenizedstr = FileFormatTokenizeID(id, file_format, inputs.render_special);
                 if(stream_sse)
                 {
                     generated_tokens.push_back(tokenizedstr);
@@ -2229,14 +2229,14 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
                         printf(" ");
                     }
                     firstloop = false;
-                    std::string tokenizedstr = FileFormatTokenizeID(pick.id, file_format);
+                    std::string tokenizedstr = FileFormatTokenizeID(pick.id, file_format, true);
                     ::utreplace(tokenizedstr, "\n", "\\n");
                     printf("(%s %.2f%%)", RemoveBell(tokenizedstr).c_str(), pick.p*100);
                 }
                 printf("]\n");
             }
 
-            if(inputs.unban_tokens_rt && id==eosID)
+            if(inputs.allow_eos_token && id==eosID)
             {
                 stopper_unused_tokens = remaining_tokens;
                 if(allow_regular_prints)
diff --git a/kcpp_docs.embd b/kcpp_docs.embd
index 7a021ae6a..d21b1a26d 100644
--- a/kcpp_docs.embd
+++ b/kcpp_docs.embd
@@ -193,9 +193,14 @@
                             "description": "KoboldCpp ONLY. If set, takes an array of base64 encoded strings, each one representing an image to be processed.",
                             "type": "array"
                          },
-                          "trim_stop": {
+                         "trim_stop": {
                             "default": false,
-                            "description": "KoboldCpp ONLY. If true, also removes detected stop_sequences from the output and truncates all text after them. Does not work with SSE streaming.",
+                            "description": "KoboldCpp ONLY. If true, also removes detected stop_sequences from the output and truncates all text after them.",
+                            "type": "boolean"
+                         },
+                         "render_special": {
+                            "default": false,
+                            "description": "KoboldCpp ONLY. If true, prints special tokens as text for GGUF models",
                             "type": "boolean"
                          },
                          "logit_bias": {
diff --git a/koboldcpp.py b/koboldcpp.py
index 94d401596..9f504f733 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -81,7 +81,8 @@ class generation_inputs(ctypes.Structure):
                 ("mirostat_eta", ctypes.c_float),
                 ("sampler_order", ctypes.c_int * sampler_order_max),
                 ("sampler_len", ctypes.c_int),
-                ("unban_tokens_rt", ctypes.c_bool),
+                ("allow_eos_token", ctypes.c_bool),
+                ("render_special", ctypes.c_bool),
                 ("stop_sequence", ctypes.c_char_p * stop_token_max),
                 ("stream_sse", ctypes.c_bool),
                 ("grammar", ctypes.c_char_p),
@@ -398,7 +399,7 @@ def load_model(model_filename):
     ret = handle.load_model(inputs)
     return ret
 
-def generate(prompt, memory="", images=[], max_length=32, max_context_length=512, temperature=0.7, top_k=100, top_a=0.0, top_p=0.92, min_p=0.0, typical_p=1.0, tfs=1.0, rep_pen=1.0, rep_pen_range=128, presence_penalty=0.0, mirostat=0, mirostat_tau=5.0, mirostat_eta=0.1, sampler_order=[6,0,1,3,4,2,5], seed=-1, stop_sequence=[], use_default_badwordsids=False, stream_sse=False, grammar='', grammar_retain_state=False, genkey='', trimstop=False, quiet=False, dynatemp_range=0.0, dynatemp_exponent=1.0, smoothing_factor=0.0, logit_biases={}):
+def generate(prompt, memory="", images=[], max_length=32, max_context_length=512, temperature=0.7, top_k=100, top_a=0.0, top_p=0.92, min_p=0.0, typical_p=1.0, tfs=1.0, rep_pen=1.0, rep_pen_range=128, presence_penalty=0.0, mirostat=0, mirostat_tau=5.0, mirostat_eta=0.1, sampler_order=[6,0,1,3,4,2,5], seed=-1, stop_sequence=[], use_default_badwordsids=False, stream_sse=False, grammar='', grammar_retain_state=False, genkey='', trimstop=False, quiet=False, dynatemp_range=0.0, dynatemp_exponent=1.0, smoothing_factor=0.0, logit_biases={}, render_special=False):
     global maxctx, args, currentusergenkey, totalgens, pendingabortkey
     inputs = generation_inputs()
     inputs.prompt = prompt.encode("UTF-8")
@@ -436,7 +437,8 @@ def generate(prompt, memory="", images=[], max_length=32, max_context_length=512
     inputs.smoothing_factor = smoothing_factor
     inputs.grammar = grammar.encode("UTF-8")
     inputs.grammar_retain_state = grammar_retain_state
-    inputs.unban_tokens_rt = not use_default_badwordsids
+    inputs.allow_eos_token = not use_default_badwordsids
+    inputs.render_special = render_special
     if mirostat in (1, 2):
         inputs.mirostat = mirostat
         inputs.mirostat_tau = mirostat_tau
@@ -806,7 +808,8 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
                 dynatemp_range=genparams.get('dynatemp_range', 0.0),
                 dynatemp_exponent=genparams.get('dynatemp_exponent', 1.0),
                 smoothing_factor=genparams.get('smoothing_factor', 0.0),
-                logit_biases=genparams.get('logit_bias', {})
+                logit_biases=genparams.get('logit_bias', {}),
+                render_special=genparams.get('render_special', False),
                 )
 
         recvtxt = ""
diff --git a/llama.cpp b/llama.cpp
index 2ba00b07d..c744f0769 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1626,12 +1626,12 @@ struct llama_mlock {
 };
 using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
 
-static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
+static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
     std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
         GGML_ASSERT(check == -n_tokens);
     }
     else {
@@ -2146,7 +2146,7 @@ struct llama_vocab {
     id special_prefix_id = -1;
     id special_suffix_id = -1;
     id special_middle_id = -1;
-    id special_eot_id    = -1;
+    id special_eot_id    = -1; // TODO: move above after "eos_id", and here add "file separator" token
 
     bool add_space_prefix = true;
     int find_bpe_rank(std::string token_left, std::string token_right) const {
@@ -3814,7 +3814,7 @@ static void llm_load_hparams(
                     switch (hparams.n_layer) {
                         case 22: model.type = e_model::MODEL_1B; break;
                         case 26: model.type = e_model::MODEL_3B; break;
-                        case 32: model.type = e_model::MODEL_7B; break;
+                        case 32: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_7B : e_model::MODEL_8B; break; // LLaMa 8B v3 uses GQA
                         case 40: model.type = e_model::MODEL_13B; break;
                         case 48: model.type = e_model::MODEL_34B; break;
                         case 60: model.type = e_model::MODEL_30B; break;
@@ -4224,7 +4224,10 @@ static void llm_load_vocab(
                     vocab.special_prefix_id = 67;
                     vocab.special_suffix_id = 69;
                     vocab.special_middle_id = 68;
-                    vocab.special_eot_id    = 70;
+                    // TODO: this is not EOT, it is "file separator" token, needs fix
+                    //       https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
+                    //vocab.special_eot_id    = 70;
+                    vocab.special_eot_id    = 107;
                 }
             }
 
@@ -4371,6 +4374,7 @@ static void llm_load_vocab(
             { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id },
             { LLM_KV_TOKENIZER_EOT_ID,    vocab.special_eot_id    },
         };
+
         for (const auto & it : special_token_types) {
             const std::string & key = kv(std::get<0>(it));
             int32_t & id = std::get<1>(it);
@@ -4385,7 +4389,6 @@ static void llm_load_vocab(
             } else {
                 id = new_id;
             }
-
         }
 
         // Handle add_bos_token and add_eos_token
@@ -4399,6 +4402,27 @@ static void llm_load_vocab(
                 vocab.special_add_eos = int(temp);
             }
         }
+
+        // find EOT token: "<|eot_id|>", "<|im_emd|>", "<end_of_turn>", etc.
+        //
+        // TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOT_ID
+        //       for now, we apply this workaround to find the EOT token based on its text
+        if (vocab.special_eot_id == -1) {
+            for (const auto & t : vocab.token_to_id) {
+                if (
+                        // TODO: gemma "<end_of_turn>" is exported as a normal token, so the following check does not work
+                        //       need to fix convert script
+                        //vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
+                        (t.first == "<|eot_id|>" ||
+                         t.first == "<|im_emd|>" ||
+                         t.first == "<end_of_turn>"
+                        )
+                   ) {
+                    vocab.special_eot_id = t.second;
+                    break;
+                }
+            }
+        }
     }
 
     // build special tokens cache
@@ -4561,14 +4585,19 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, model.name.c_str());
 
     // special tokens
-    if (vocab.special_bos_id  != -1) { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id,  vocab.id_to_token[vocab.special_bos_id].text.c_str() );  }
-    if (vocab.special_eos_id  != -1) { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id,  vocab.id_to_token[vocab.special_eos_id].text.c_str() );  }
-    if (vocab.special_unk_id  != -1) { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id,  vocab.id_to_token[vocab.special_unk_id].text.c_str() );  }
-    if (vocab.special_sep_id  != -1) { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id,  vocab.id_to_token[vocab.special_sep_id].text.c_str() );  }
-    if (vocab.special_pad_id  != -1) { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id,  vocab.id_to_token[vocab.special_pad_id].text.c_str() );  }
-    if (vocab.special_cls_id  != -1) { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, vocab.special_cls_id,  vocab.id_to_token[vocab.special_cls_id].text.c_str() );  }
-    if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
-    if (vocab.linefeed_id     != -1) { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,     vocab.id_to_token[vocab.linefeed_id].text.c_str() );     }
+    if (vocab.special_bos_id    != -1) { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id,  vocab.id_to_token[vocab.special_bos_id].text.c_str() );  }
+    if (vocab.special_eos_id    != -1) { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id,  vocab.id_to_token[vocab.special_eos_id].text.c_str() );  }
+    if (vocab.special_unk_id    != -1) { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id,  vocab.id_to_token[vocab.special_unk_id].text.c_str() );  }
+    if (vocab.special_sep_id    != -1) { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id,  vocab.id_to_token[vocab.special_sep_id].text.c_str() );  }
+    if (vocab.special_pad_id    != -1) { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id,  vocab.id_to_token[vocab.special_pad_id].text.c_str() );  }
+    if (vocab.special_cls_id    != -1) { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, vocab.special_cls_id,  vocab.id_to_token[vocab.special_cls_id].text.c_str() );  }
+    if (vocab.special_mask_id   != -1) { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
+
+    if (vocab.linefeed_id       != -1) { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,       vocab.id_to_token[vocab.linefeed_id].text.c_str() );       }
+    if (vocab.special_prefix_id != -1) { LLAMA_LOG_INFO( "%s: PRE token        = %d '%s'\n", __func__, vocab.special_prefix_id, vocab.id_to_token[vocab.special_prefix_id].text.c_str() ); }
+    if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token        = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
+    if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token        = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
+    if (vocab.special_eot_id    != -1) { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,    vocab.id_to_token[vocab.special_eot_id].text.c_str() );    }
 }
 
 // Returns false if cancelled by progress_callback
@@ -13583,16 +13612,14 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
     GGML_ASSERT(ctx);
     const int64_t t_start_sample_us = ggml_time_us();
 
-    bool allow_eos = false;
+    bool allow_eog = false;
     for (const auto & stack : grammar->stacks) {
         if (stack.empty()) {
-            allow_eos = true;
+            allow_eog = true;
             break;
         }
     }
 
-    const llama_token eos = llama_token_eos(&ctx->model);
-
     std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
     candidates_decoded.reserve(candidates->size);
     std::vector<llama_grammar_candidate>                              candidates_grammar;
@@ -13600,9 +13627,10 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
 
     for (size_t i = 0; i < candidates->size; ++i) {
         const llama_token id    = candidates->data[i].id;
-        const std::string piece = llama_token_to_str(ctx, id);
-        if (id == eos) {
-            if (!allow_eos) {
+        const std::string piece = llama_token_to_piece(ctx, id, false);
+
+        if (llama_token_is_eog(&ctx->model, id)) {
+            if (!allow_eog) {
                 candidates->data[i].logit = -INFINITY;
             }
         } else if (piece.empty() || piece[0] == 0) {
@@ -13791,7 +13819,7 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra
 void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
     const int64_t t_start_sample_us = ggml_time_us();
 
-    if (token == llama_token_eos(&ctx->model)) {
+    if (llama_token_is_eog(&ctx->model, token)) {
         for (const auto & stack : grammar->stacks) {
             if (stack.empty()) {
                 return;
@@ -13800,7 +13828,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
         GGML_ASSERT(false);
     }
 
-    const std::string piece = llama_token_to_str(ctx, token);
+    const std::string piece = llama_token_to_piece(ctx, token, false);
 
     // Note terminating 0 in decoded string
     const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
@@ -17196,6 +17224,13 @@ llama_token_type llama_token_get_type(const struct llama_model * model, llama_to
     return model->vocab.id_to_token[token].type;
 }
 
+bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
+    return token != -1 && (
+        token == llama_token_eos(model) ||
+        token == llama_token_eot(model)
+    );
+}
+
 llama_token llama_token_bos(const struct llama_model * model) {
     return model->vocab.special_bos_id;
 }
@@ -17273,12 +17308,11 @@ static std::string llama_decode_text(const std::string & text) {
 }
 
 // does not write null-terminator to buf
-int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
+int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
     if(OldBPETokenizerMode)
     {
         return llama_token_to_piece_old(model, token, buf, length);
     }
-
     if (0 <= token && token < llama_n_vocab(model)) {
         switch (llama_vocab_get_type(model->vocab)) {
         case LLAMA_VOCAB_TYPE_WPM:
@@ -17293,7 +17327,9 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
-            } else if (llama_is_user_defined_token(model->vocab, token)) {
+            } else if (
+                    (llama_is_user_defined_token(model->vocab, token)) ||
+                    (llama_is_control_token     (model->vocab, token) && special)) {
                 std::string result = model->vocab.id_to_token[token].text;
                 if (length < (int) result.length()) {
                     return -(int) result.length();
@@ -17306,8 +17342,6 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, "\xe2\x96\x85", 3);
                 return 3;
-            } else if (llama_is_control_token(model->vocab, token)) {
-                ;
             } else if (llama_is_byte_token(model->vocab, token)) {
                 if (length < 1) {
                     return -1;
@@ -17328,15 +17362,15 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
-            } else if (llama_is_user_defined_token(model->vocab, token)) {
+            } else if (
+                    (llama_is_user_defined_token(model->vocab, token)) ||
+                    (llama_is_control_token     (model->vocab, token) && special)) {
                 std::string result = model->vocab.id_to_token[token].text;
                 if (length < (int) result.length()) {
                     return -(int) result.length();
                 }
                 memcpy(buf, result.c_str(), result.length());
                 return result.length();
-            } else if (llama_is_control_token(model->vocab, token)) {
-                ;
             }
             break;
         }
@@ -17534,6 +17568,15 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
         }
+    } else if (tmpl == "llama3" || (tmpl.find("<|start_header_id|>") != std::string::npos && tmpl.find("<|end_header_id|>") != std::string::npos)) {
+        // Llama 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>";
+        }
+        if (add_ass) {
+            ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
+        }
     } else {
         // template not supported
         return -1;
diff --git a/llama.h b/llama.h
index 0fd44bf61..6817f1014 100644
--- a/llama.h
+++ b/llama.h
@@ -785,6 +785,9 @@ extern "C" {
 
     LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
 
+    // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
+    LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
+
     // Special tokens
     LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
     LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
@@ -798,7 +801,7 @@ extern "C" {
     // Returns -1 if unknown, 1 for true or 0 for false.
     LLAMA_API int32_t         llama_add_eos_token(const struct llama_model * model);
 
-    // codellama infill tokens
+    // Codellama infill tokens
     LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
     LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
     LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
@@ -827,11 +830,13 @@ extern "C" {
     // Uses the vocabulary in the provided context.
     // Does not write null terminator to the buffer.
     // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
+    // @param special If true, special tokens are rendered in the output.
     LLAMA_API int32_t llama_token_to_piece(
               const struct llama_model * model,
                            llama_token   token,
                                   char * buf,
-                               int32_t   length);
+                               int32_t   length,
+                                  bool   special);
 
     /// Apply chat template. Inspired by hf apply_chat_template() on python.
     /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
diff --git a/scripts/xxd.cmake b/scripts/xxd.cmake
new file mode 100644
index 000000000..f5ad6ab9b
--- /dev/null
+++ b/scripts/xxd.cmake
@@ -0,0 +1,16 @@
+# CMake equivalent of `xxd -i ${INPUT} ${OUTPUT}`
+# Usage: cmake -DINPUT=examples/server/public/index.html -DOUTPUT=examples/server/index.html.hpp -P scripts/xxd.cmake
+
+SET(INPUT "" CACHE STRING "Input File")
+SET(OUTPUT "" CACHE STRING "Output File")
+
+get_filename_component(filename "${INPUT}" NAME)
+string(REGEX REPLACE "\\.|-" "_" name "${filename}")
+
+file(READ "${INPUT}" hex_data HEX)
+string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," hex_sequence "${hex_data}")
+
+string(LENGTH ${hex_data} hex_len)
+math(EXPR len "${hex_len} / 2")
+
+file(WRITE "${OUTPUT}" "unsigned char ${name}[] = {${hex_sequence}};\nunsigned int ${name}_len = ${len};\n")