fix crash in bindings

llama/CMakeLists.txt

@@ -4,7 +4,7 @@ include(FetchContent)
 FetchContent_Declare(
     llama_cpp
     GIT_REPOSITORY https://github.com/ggerganov/llama.cpp.git
-    GIT_TAG        master
+    GIT_TAG        55dbb91
 )
 
 FetchContent_MakeAvailable(llama_cpp)

llama/binding/binding.cpp

@@ -1,25 +1,3 @@
-// MIT License
-
-// Copyright (c) 2023 go-skynet authors
-
-// Permission is hereby granted, free of charge, to any person obtaining a copy
-// of this software and associated documentation files (the "Software"), to deal
-// in the Software without restriction, including without limitation the rights
-// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-// copies of the Software, and to permit persons to whom the Software is
-// furnished to do so, subject to the following conditions:
-
-// The above copyright notice and this permission notice shall be included in
-// all copies or substantial portions of the Software.
-
-// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-// SOFTWARE.
-
 #include "common.h"
 #include "llama.h"
 
@@ -55,14 +33,78 @@ void sigint_handler(int signo) {
 }
 #endif
 
-int eval(void *p, void *c, char *text) {
-  gpt_params *params = (gpt_params *)params;
-  llama_context *ctx = (llama_context *)ctx;
+int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings) {
+  gpt_params *params_p = (gpt_params *)params_ptr;
+  llama_context *ctx = (llama_context *)state_pr;
+  gpt_params params = *params_p;
+
+  if (params.seed <= 0) {
+    params.seed = time(NULL);
+  }
+
+  std::mt19937 rng(params.seed);
+
+  llama_init_backend(params.numa);
+
+  int n_past = 0;
+
+  // Add a space in front of the first character to match OG llama tokenizer
+  // behavior
+  params.prompt.insert(0, 1, ' ');
+
+  // tokenize the prompt
+  auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
+
+  // determine newline token
+  auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
+
+  if (embd_inp.size() > 0) {
+    if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past,
+                   params.n_threads)) {
+      fprintf(stderr, "%s : failed to eval\n", __func__);
+      return 1;
+    }
+  }
+
+  const int n_embd = llama_n_embd(ctx);
+
+  const auto embeddings = llama_get_embeddings(ctx);
+
+  for (int i = 0; i < n_embd; i++) {
+    res_embeddings[i] = embeddings[i];
+  }
+
+  return 0;
+}
+
+int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
+                         int tokenSize, float *res_embeddings) {
+  gpt_params *params_p = (gpt_params *)params_ptr;
+  llama_context *ctx = (llama_context *)state_pr;
+  gpt_params params = *params_p;
+
+  for (int i = 0; i < tokenSize; i++) {
+    auto token_str = llama_token_to_str(ctx, tokens[i]);
+    if (token_str == nullptr) {
+      continue;
+    }
+    std::vector<std::string> my_vector;
+    std::string str_token(token_str); // create a new std::string from the char*
+    params_p->prompt += str_token;
+  }
+
+  return get_embeddings(params_ptr, state_pr, res_embeddings);
+}
+
+int eval(void *params_ptr, void *state_pr, char *text) {
+  gpt_params *params_p = (gpt_params *)params_ptr;
+  llama_context *ctx = (llama_context *)state_pr;
 
   auto n_past = 0;
-  auto last_n_tokens_data = std::vector<llama_token>(params->repeat_last_n, 0);
+  auto last_n_tokens_data =
+      std::vector<llama_token>(params_p->repeat_last_n, 0);
 
-  auto tokens = std::vector<llama_token>(params->n_ctx);
+  auto tokens = std::vector<llama_token>(params_p->n_ctx);
   auto n_prompt_tokens =
       llama_tokenize(ctx, text, tokens.data(), tokens.size(), true);
 
@@ -71,22 +113,26 @@ int eval(void *p, void *c, char *text) {
     return 1;
   }
 
+  // evaluate prompt
   return llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past,
-                    params->n_threads);
+                    params_p->n_threads);
 }
 
-int llama_predict(void *p, void *c, char *result, bool debug) {
-  gpt_params *params = (gpt_params *)params;
-  llama_context *ctx = (llama_context *)ctx;
+int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
+  gpt_params *params_p = (gpt_params *)params_ptr;
+  llama_context *ctx = (llama_context *)state_pr;
+
+  gpt_params params = *params_p;
 
   const int n_ctx = llama_n_ctx(ctx);
 
-  if (params->seed <= 0) {
-    params->seed = time(NULL);
+  if (params.seed <= 0) {
+    params.seed = time(NULL);
   }
 
-  std::mt19937 rng(params->seed);
-  std::string path_session = params->path_prompt_cache;
+  std::mt19937 rng(params.seed);
+
+  std::string path_session = params.path_prompt_cache;
   std::vector<llama_token> session_tokens;
 
   if (!path_session.empty()) {
@@ -109,7 +155,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
         return 1;
       }
       session_tokens.resize(n_token_count_out);
-      llama_set_rng_seed(ctx, params->seed);
+      llama_set_rng_seed(ctx, params.seed);
       if (debug) {
         fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n",
                 __func__, (int)session_tokens.size());
@@ -123,12 +169,12 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
   }
 
   std::vector<llama_token> embd_inp;
-  if (!params->prompt.empty() || session_tokens.empty()) {
+  if (!params.prompt.empty() || session_tokens.empty()) {
     // Add a space in front of the first character to match OG llama tokenizer
     // behavior
-    params->prompt.insert(0, 1, ' ');
+    params.prompt.insert(0, 1, ' ');
 
-    embd_inp = ::llama_tokenize(ctx, params->prompt, true);
+    embd_inp = ::llama_tokenize(ctx, params.prompt, true);
   } else {
     embd_inp = session_tokens;
   }
@@ -144,7 +190,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
       n_matching_session_tokens++;
     }
     if (debug) {
-      if (params->prompt.empty() &&
+      if (params.prompt.empty() &&
           n_matching_session_tokens == embd_inp.size()) {
         fprintf(stderr, "%s: using full prompt from session file\n", __func__);
       } else if (n_matching_session_tokens >= embd_inp.size()) {
@@ -169,8 +215,8 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
     session_tokens.resize(embd_inp.size() - 1);
   }
   // number of tokens to keep when resetting context
-  if (params->n_keep < 0 || params->n_keep > (int)embd_inp.size()) {
-    params->n_keep = (int)embd_inp.size();
+  if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size()) {
+    params.n_keep = (int)embd_inp.size();
   }
 
   // determine newline token
@@ -183,7 +229,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
   bool need_to_save_session =
       !path_session.empty() && n_matching_session_tokens < embd_inp.size();
   int n_past = 0;
-  int n_remain = params->n_predict;
+  int n_remain = params.n_predict;
   int n_consumed = 0;
   int n_session_consumed = 0;
 
@@ -195,7 +241,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
     const std::vector<llama_token> tmp = {
         llama_token_bos(),
     };
-    llama_eval(ctx, tmp.data(), tmp.size(), 0, params->n_threads);
+    llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
     llama_reset_timings(ctx);
   }
 
@@ -208,10 +254,10 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
       // - take half of the last (n_ctx - n_keep) tokens and recompute the
       // logits in batches
       if (n_past + (int)embd.size() > n_ctx) {
-        const int n_left = n_past - params->n_keep;
+        const int n_left = n_past - params.n_keep;
 
         // always keep the first token - BOS
-        n_past = std::max(1, params->n_keep);
+        n_past = std::max(1, params.n_keep);
 
         // insert n_left/2 tokens at the start of embd from last_n_tokens
         embd.insert(embd.begin(),
@@ -220,6 +266,14 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
 
         // stop saving session if we run out of context
         path_session.clear();
+
+        // printf("\n---\n");
+        // printf("resetting: '");
+        // for (int i = 0; i < (int) embd.size(); i++) {
+        //     printf("%s", llama_token_to_str(ctx, embd[i]));
+        // }
+        // printf("'\n");
+        // printf("\n---\n");
       }
 
       // try to reuse a matching prefix from the loaded session instead of
@@ -248,17 +302,15 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
       // evaluate tokens in batches
       // embd is typically prepared beforehand to fit within a batch, but not
       // always
-      for (int i = 0; i < (int)embd.size(); i += params->n_batch) {
+      for (int i = 0; i < (int)embd.size(); i += params.n_batch) {
         int n_eval = (int)embd.size() - i;
-        if (n_eval > params->n_batch) {
-          n_eval = params->n_batch;
+        if (n_eval > params.n_batch) {
+          n_eval = params.n_batch;
         }
-
-        if (llama_eval(ctx, &embd[i], n_eval, n_past, params->n_threads)) {
+        if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
           fprintf(stderr, "%s : failed to eval\n", __func__);
           return 1;
         }
-
         n_past += n_eval;
       }
 
@@ -272,26 +324,26 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
 
     if ((int)embd_inp.size() <= n_consumed) {
       // out of user input, sample next token
-      const float temp = params->temp;
+      const float temp = params.temp;
       const int32_t top_k =
-          params->top_k <= 0 ? llama_n_vocab(ctx) : params->top_k;
-      const float top_p = params->top_p;
-      const float tfs_z = params->tfs_z;
-      const float typical_p = params->typical_p;
+          params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
+      const float top_p = params.top_p;
+      const float tfs_z = params.tfs_z;
+      const float typical_p = params.typical_p;
       const int32_t repeat_last_n =
-          params->repeat_last_n < 0 ? n_ctx : params->repeat_last_n;
-      const float repeat_penalty = params->repeat_penalty;
-      const float alpha_presence = params->presence_penalty;
-      const float alpha_frequency = params->frequency_penalty;
-      const int mirostat = params->mirostat;
-      const float mirostat_tau = params->mirostat_tau;
-      const float mirostat_eta = params->mirostat_eta;
-      const bool penalize_nl = params->penalize_nl;
+          params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
+      const float repeat_penalty = params.repeat_penalty;
+      const float alpha_presence = params.presence_penalty;
+      const float alpha_frequency = params.frequency_penalty;
+      const int mirostat = params.mirostat;
+      const float mirostat_tau = params.mirostat_tau;
+      const float mirostat_eta = params.mirostat_eta;
+      const bool penalize_nl = params.penalize_nl;
 
       // optionally save the session on first sample (for faster prompt loading
       // next time)
       if (!path_session.empty() && need_to_save_session &&
-          !params->prompt_cache_ro) {
+          !params.prompt_cache_ro) {
         need_to_save_session = false;
         llama_save_session_file(ctx, path_session.c_str(),
                                 session_tokens.data(), session_tokens.size());
@@ -304,8 +356,8 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
         auto n_vocab = llama_n_vocab(ctx);
 
         // Apply params.logit_bias map
-        for (auto it = params->logit_bias.begin();
-             it != params->logit_bias.end(); it++) {
+        for (auto it = params.logit_bias.begin(); it != params.logit_bias.end();
+             it++) {
           logits[it->first] += it->second;
         }
 
@@ -361,6 +413,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
             id = llama_sample_token(ctx, &candidates_p);
           }
         }
+        // printf("`%d`", candidates_p.size);
 
         last_n_tokens.erase(last_n_tokens.begin());
         last_n_tokens.push_back(id);
@@ -375,7 +428,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
       // call the token callback, no need to check if one is actually
       // registered, that will be handled on the Go side.
       auto token_str = llama_token_to_str(ctx, id);
-      if (!tokenCallback(ctx, (char *)token_str)) {
+      if (!tokenCallback(state_pr, (char *)token_str)) {
         break;
       }
     } else {
@@ -386,7 +439,7 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
         last_n_tokens.erase(last_n_tokens.begin());
         last_n_tokens.push_back(embd_inp[n_consumed]);
         ++n_consumed;
-        if ((int)embd.size() >= params->n_batch) {
+        if ((int)embd.size() >= params.n_batch) {
           break;
         }
       }
@@ -397,13 +450,13 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
     }
 
     // check for stop prompt
-    if (params->antiprompt.size()) {
+    if (params.antiprompt.size()) {
       std::string last_output;
       for (auto id : last_n_tokens) {
         last_output += llama_token_to_str(ctx, id);
       }
       // Check if each of the reverse prompts appears at the end of the output.
-      for (std::string &antiprompt : params->antiprompt) {
+      for (std::string &antiprompt : params.antiprompt) {
         // size_t extra_padding = params.interactive ? 0 : 2;
         size_t extra_padding = 2;
         size_t search_start_pos =
@@ -426,8 +479,8 @@ int llama_predict(void *p, void *c, char *result, bool debug) {
     }
   }
 
-  if (!path_session.empty() && params->prompt_cache_all &&
-      !params->prompt_cache_ro) {
+  if (!path_session.empty() && params.prompt_cache_all &&
+      !params.prompt_cache_ro) {
     if (debug) {
       fprintf(stderr, "\n%s: saving final output to session file '%s'\n",
               __func__, path_session.c_str());
@@ -450,8 +503,68 @@ end:
   return 0;
 }
 
-void llama_binding_free_model(void *ctx) { llama_free((llama_context *)ctx); }
-void llama_free_params(void *params) { delete (gpt_params *)params; }
+void llama_binding_free_model(void *state_ptr) {
+  llama_context *ctx = (llama_context *)state_ptr;
+  llama_free(ctx);
+}
+
+void llama_free_params(void *params_ptr) {
+  gpt_params *params = (gpt_params *)params_ptr;
+  delete params;
+}
+
+std::vector<std::string> create_vector(const char **strings, int count) {
+  std::vector<std::string> *vec = new std::vector<std::string>;
+  for (int i = 0; i < count; i++) {
+    vec->push_back(std::string(strings[i]));
+  }
+  return *vec;
+}
+
+void delete_vector(std::vector<std::string> *vec) { delete vec; }
+
+int load_state(void *ctx, char *statefile, char *modes) {
+  llama_context *state = (llama_context *)ctx;
+  const llama_context *constState = static_cast<const llama_context *>(state);
+  const size_t state_size = llama_get_state_size(state);
+  uint8_t *state_mem = new uint8_t[state_size];
+
+  {
+    FILE *fp_read = fopen(statefile, modes);
+    if (state_size != llama_get_state_size(constState)) {
+      fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
+      return 1;
+    }
+
+    const size_t ret = fread(state_mem, 1, state_size, fp_read);
+    if (ret != state_size) {
+      fprintf(stderr, "\n%s : failed to read state\n", __func__);
+      return 1;
+    }
+
+    llama_set_state_data(
+        state, state_mem); // could also read directly from memory mapped file
+    fclose(fp_read);
+  }
+
+  return 0;
+}
+
+void save_state(void *ctx, char *dst, char *modes) {
+  llama_context *state = (llama_context *)ctx;
+
+  const size_t state_size = llama_get_state_size(state);
+  uint8_t *state_mem = new uint8_t[state_size];
+
+  // Save state (rng, logits, embedding and kv_cache) to file
+  {
+    FILE *fp_write = fopen(dst, modes);
+    llama_copy_state_data(
+        state, state_mem); // could also copy directly to memory mapped file
+    fwrite(state_mem, 1, state_size, fp_write);
+    fclose(fp_write);
+  }
+}
 
 void *llama_allocate_params(
     const char *prompt, int seed, int threads, int tokens, int top_k,
@@ -505,13 +618,9 @@ void *llama_allocate_params(
   if (ignore_eos) {
     params->logit_bias[llama_token_eos()] = -INFINITY;
   }
-
   if (antiprompt_count > 0) {
-    for (int i = 0; i < antiprompt_count; i++) {
-      params->antiprompt.push_back(std::string(antiprompt[i]));
-    }
+    params->antiprompt = create_vector(antiprompt, antiprompt_count);
   }
-
   params->tfs_z = tfs_z;
   params->typical_p = typical_p;
   params->presence_penalty = presence_penalty;
@@ -519,7 +628,6 @@ void *llama_allocate_params(
   params->mirostat_eta = mirostat_eta;
   params->mirostat_tau = mirostat_tau;
   params->penalize_nl = penalize_nl;
-
   std::stringstream ss(logit_bias);
   llama_token key;
   char sign;
@@ -539,6 +647,7 @@ void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
                  bool mlock, bool embeddings, bool mmap, bool low_vram,
                  bool vocab_only, int n_gpu_layers, int n_batch,
                  const char *maingpu, const char *tensorsplit, bool numa) {
+  // load the model
   auto lparams = llama_context_default_params();
 
   lparams.n_ctx = n_ctx;
@@ -575,11 +684,13 @@ void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
   lparams.n_batch = n_batch;
 
   llama_init_backend(numa);
-
-  struct llama_model *model = llama_load_model_from_file(fname, lparams);
-  if (!model) {
-    return nullptr;
+  void *res = nullptr;
+  try {
+    res = llama_init_from_file(fname, lparams);
+  } catch (std::runtime_error &e) {
+    fprintf(stderr, "failed %s", e.what());
+    return res;
   }
 
-  return llama_new_context_with_model(model, lparams);
-}
+  return res;
+}

llama/binding/binding.h

@@ -1,25 +1,3 @@
-// MIT License
-
-// Copyright (c) 2023 go-skynet authors
-
-// Permission is hereby granted, free of charge, to any person obtaining a copy
-// of this software and associated documentation files (the "Software"), to deal
-// in the Software without restriction, including without limitation the rights
-// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-// copies of the Software, and to permit persons to whom the Software is
-// furnished to do so, subject to the following conditions:
-
-// The above copyright notice and this permission notice shall be included in
-// all copies or substantial portions of the Software.
-
-// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-// SOFTWARE.
-
 #ifdef __cplusplus
 #include <string>
 #include <vector>
@@ -30,13 +8,22 @@ extern "C" {
 
 extern unsigned char tokenCallback(void *, char *);
 
-int eval(void *p, void *c, char *text);
+int load_state(void *ctx, char *statefile, char *modes);
+
+int eval(void *params_ptr, void *ctx, char *text);
+
+void save_state(void *ctx, char *dst, char *modes);
 
 void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
                  bool mlock, bool embeddings, bool mmap, bool low_vram,
                  bool vocab_only, int n_gpu, int n_batch, const char *maingpu,
                  const char *tensorsplit, bool numa);
 
+int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings);
+
+int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
+                         int tokenSize, float *res_embeddings);
+
 void *llama_allocate_params(
     const char *prompt, int seed, int threads, int tokens, int top_k,
     float top_p, float temp, float repeat_penalty, int repeat_last_n,
@@ -50,11 +37,13 @@ void *llama_allocate_params(
 
 void llama_free_params(void *params_ptr);
 
-void llama_binding_free_model(void *ctx);
+void llama_binding_free_model(void *state);
 
 int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug);
 
 #ifdef __cplusplus
 }
 
-#endif
+std::vector<std::string> create_vector(const char **strings, int count);
+void delete_vector(std::vector<std::string> *vec);
+#endif

llama/llama.go

@@ -28,6 +28,7 @@ package llama
 // #cgo CXXFLAGS: -std=c++11
 // #cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
 // #include "binding/binding.h"
+// #include <stdlib.h>
 import "C"
 import (
 	"fmt"
@@ -45,8 +46,8 @@ type LLama struct {
 func New(model string, opts ...ModelOption) (*LLama, error) {
 	mo := NewModelOptions(opts...)
 
-	// TODO: free this pointer
 	modelPath := C.CString(model)
+	defer C.free(unsafe.Pointer(modelPath))
 
 	ctx := C.load_model(modelPath, C.int(mo.ContextSize), C.int(mo.Seed), C.bool(mo.F16Memory), C.bool(mo.MLock), C.bool(mo.Embeddings), C.bool(mo.MMap), C.bool(mo.LowVRAM), C.bool(mo.VocabOnly), C.int(mo.NGPULayers), C.int(mo.NBatch), C.CString(mo.MainGPU), C.CString(mo.TensorSplit), C.bool(mo.NUMA))
 	if ctx == nil {
@@ -69,7 +70,7 @@ func (l *LLama) Eval(text string, opts ...PredictOption) error {
 	if po.Tokens == 0 {
 		po.Tokens = 99999999
 	}
-
+	
 	reverseCount := len(po.StopPrompts)
 	reversePrompt := make([]*C.char, reverseCount)
 	var pass **C.char
@@ -94,24 +95,34 @@ func (l *LLama) Eval(text string, opts ...PredictOption) error {
 		return fmt.Errorf("inference failed")
 	}
 
+	fmt.Println("hi 4")
+
 	C.llama_free_params(params)
 
+	fmt.Println("hi 5")
+
 	return nil
 }
 
 func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
 	po := NewPredictOptions(opts...)
 
+	fmt.Println("predict 1")
+
 	if po.TokenCallback != nil {
 		setCallback(l.ctx, po.TokenCallback)
 	}
 
+	fmt.Println("predict 2")
+
 	input := C.CString(text)
 	if po.Tokens == 0 {
 		po.Tokens = 99999999
 	}
 	out := make([]byte, po.Tokens)
 
+	fmt.Println("predict 3")
+
 	reverseCount := len(po.StopPrompts)
 	reversePrompt := make([]*C.char, reverseCount)
 	var pass **C.char
@@ -121,6 +132,8 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
 		pass = &reversePrompt[0]
 	}
 
+	fmt.Println("predict 4")
+
 	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
 		C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
 		C.bool(po.IgnoreEOS), C.bool(po.F16KV),
@@ -131,12 +144,16 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
 		C.CString(po.MainGPU), C.CString(po.TensorSplit),
 		C.bool(po.PromptCacheRO),
 	)
+
+	fmt.Println("predict 4.5")
 	ret := C.llama_predict(params, l.ctx, (*C.char)(unsafe.Pointer(&out[0])), C.bool(po.DebugMode))
 	if ret != 0 {
 		return "", fmt.Errorf("inference failed")
 	}
 	res := C.GoString((*C.char)(unsafe.Pointer(&out[0])))
 
+	fmt.Println("predict 5")
+
 	res = strings.TrimPrefix(res, " ")
 	res = strings.TrimPrefix(res, text)
 	res = strings.TrimPrefix(res, "\n")
@@ -145,6 +162,8 @@ func (l *LLama) Predict(text string, opts ...PredictOption) (string, error) {
 		res = strings.TrimRight(res, s)
 	}
 
+	fmt.Println("predict 6")
+
 	C.llama_free_params(params)
 
 	if po.TokenCallback != nil {