5 files changed, 13 insertions, 11 deletions
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index 62f5ce3c..11f7410e 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -230,7 +230,7 @@ int main(int argc, char ** argv) {
         LOG_TEE("\n");
         LOG_TEE("%s\n", get_system_info(params).c_str());
     }
-    const bool add_bos = llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM;
+    const bool add_bos = llama_should_add_bos_token(model);
     LOG("add_bos: %d\n", add_bos);
 
     bool suff_rm_leading_spc = params.escape;
diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp
index 633afd1d..31f8cd8e 100644
--- a/examples/llava/llava-cli.cpp
+++ b/examples/llava/llava-cli.cpp
@@ -208,9 +208,10 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
     int n_past = 0;
 
     const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
+    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama));
 
     // llava chat format is "<system_prompt>\nUSER:<image_embeddings>\n<textual_prompt>\nASSISTANT:"
-    eval_string(ctx_llava->ctx_llama, "A chat between a curious human and an artificial intelligence assistant.  The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params->n_batch, &n_past, true);
+    eval_string(ctx_llava->ctx_llama, "A chat between a curious human and an artificial intelligence assistant.  The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params->n_batch, &n_past, add_bos);
     llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
     eval_string(ctx_llava->ctx_llama, (prompt + "\nASSISTANT:").c_str(), params->n_batch, &n_past, false);
 
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 8d985c82..99d219d6 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -229,7 +229,7 @@ int main(int argc, char ** argv) {
         }
     }
 
-    const bool add_bos = llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM;
+    const bool add_bos = llama_should_add_bos_token(model);
     LOG("add_bos: %d\n", add_bos);
 
     std::vector<llama_token> embd_inp;
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index de60c522..9a77beca 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -149,8 +149,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
     // Output: `perplexity: 13.5106 [114/114]`
     // BOS tokens will be added for each chunk before eval
 
-    const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM;
-    const bool add_bos = is_spm;
+    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
 
     fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
 
@@ -288,8 +287,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
     // Output: `perplexity: 13.5106 [114/114]`
     // BOS tokens will be added for each chunk before eval
 
-    const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM;
-    const bool add_bos = is_spm;
+    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
     const int n_ctx = llama_n_ctx(ctx);
 
     auto tim1 = std::chrono::high_resolution_clock::now();
@@ -481,7 +479,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     fprintf(stderr, "================================= is_spm = %d\n", is_spm);
 
     // This is needed as usual for LLaMA models
-    const bool add_bos = is_spm;
+    const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
 
     // Number of tasks to use when computing the score
     if ( params.hellaswag_tasks < hs_task_count  ) {
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 46862a84..bb87b532 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -501,6 +501,7 @@ struct llama_server_context
     bool multimodal         = false;
     bool clean_kv_cache     = true;
     bool all_slots_are_idle = false;
+    bool add_bos_token      = true;
 
     int32_t id_gen;
     int32_t n_ctx;  // total context for all clients / slots
@@ -573,6 +574,8 @@ struct llama_server_context
 
         n_ctx = llama_n_ctx(ctx);
 
+        add_bos_token = llama_should_add_bos_token(model);
+
         return true;
     }
 
@@ -864,7 +867,7 @@ struct llama_server_context
     }
 
     void update_system_prompt() {
-        system_tokens = ::llama_tokenize(ctx, system_prompt, true);
+        system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
 
         llama_batch_clear(batch);
 
@@ -1552,7 +1555,7 @@ struct llama_server_context
                     }
                     else
                     {
-                        prompt_tokens = tokenize(slot.prompt, system_prompt.empty());  // add BOS if there isn't system prompt
+                        prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token);  // add BOS if there isn't system prompt
                     }
 
                     slot.num_prompt_tokens = prompt_tokens.size();
@@ -1629,7 +1632,7 @@ struct llama_server_context
                     const bool has_images = process_images(slot);
 
                     // process the prefix of first image
-                    std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens;
+                    std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
                     for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
                     {
                        llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false);