.gitmodules

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+[submodule "stream/thirdparty/llama.cpp"]
+	path = stream/thirdparty/llama.cpp
+	url = https://github.com/ggml-org/llama.cpp

stream/.gitignore

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+.cache
+stream_app
+*.gguf
+compile_commands.json

stream/Makefile

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+CC = gcc
+CFLAGS = -Wall -g -I./thirdparty/llama.cpp/include -I./thirdparty/llama.cpp/ggml/include
+TARGET = stream_app
+SRC = main.c
+LIBS = -L./thirdparty/llama.cpp/build/bin -lllama -lggml -lggml-base
+
+$(TARGET): $(SRC)
+	$(CC) $(CFLAGS) -o $(TARGET) $(SRC) $(LIBS)
+
+clean:
+	rm -f *.o $(TARGET)

stream/README.md

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+# flowy.stream
+
+This is a proof of concept of a showing widgets within a conversation based on intent.
+
+## Todo
+- [x] Integrate llama.cpp with local inference. This will set us up for building many parts of experience.
+- [ ] Disect what llama is doing and what the Phi model is doing.
+- [ ] Create conversational loop with chat and running context.
+- [ ] Generate & render different types of blocks: list, email, doc, etc.
+- [ ] Try different models with hugging face
+- [ ] Render TUI elements based on commands
+
+## Dependencies
+
+### llama.cpp
+```sh
+#model weights
+wget https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/resolve/main/Phi-3-mini-4k-instruct-q4.gguf
+```
+
+```sh
+cd ./thirdparty/llama.cpp
+
+rm -rf build
+mkdir -p build
+cmake -S. -Bbuild -DLLAMA_CURL=OFF
+cmake --build build
+```
+
+The libraries should be in `./thirdparty/llama.cpp/build/bin`.
+
+NOTE: you may have to disable curl as a flag when configuring llama.cpp via cmake.
+
+## Getting started
+```sh
+make
+LD_LIBRARY_PATH=./thirdparty/llama.cpp/build/bin/ ./stream_app
+```

stream/main.c

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+#include "thirdparty/llama.cpp/ggml/include/ggml-backend.h"
+#include <llama.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+typedef enum {
+  Unspecified,
+  Question,
+  Math,
+  Code,
+  Email,
+  Doc,
+  WebSearch,
+} IntentType;
+
+int main(int argc, char *argv[]) {
+  printf("Hello world\n");
+
+  char user_input[500];
+  printf("What's up, what can I help with?\n");
+  fgets(user_input, sizeof(user_input), stdin);
+
+  char prompt[1000];
+  snprintf(prompt, sizeof(prompt), "<|system|>You are a helpful assistant. Your goal is to take in what the user is doing and return 3 predictive actions/3 suggestions based on what the user is trying to do: e.g. change page title when in google sheets, or calculate sum, create chart.<|end|>\n<|user|>%s<|end|>\n<|assistant|>", user_input);
+
+  // number of layers to offload to the GPU
+  int ngl = 99;
+  // number of tokens to predict
+  int n_predict = 1000;
+
+  // load dynamic backends
+
+  ggml_backend_load_all();
+
+  // initialize the model
+
+  struct llama_model_params model_params = llama_model_default_params();
+  model_params.n_gpu_layers = ngl;
+
+  struct llama_model *model = llama_model_load_from_file(
+      "Phi-3-mini-4k-instruct-q4.gguf", model_params);
+
+  if (model == NULL) {
+    fprintf(stderr, "%s: error: unable to load model\n", __func__);
+    return 1;
+  }
+
+  const struct llama_vocab *vocab = llama_model_get_vocab(model);
+  // tokenize the prompt
+
+  // find the number of tokens in the prompt
+  const int n_prompt =
+      -llama_tokenize(vocab, prompt, strlen(prompt), NULL, 0, true, true);
+
+  // allocate space for the tokens and tokenize the prompt
+  llama_token *prompt_tokens = malloc(n_prompt * sizeof(llama_token));
+  if (prompt_tokens == NULL) {
+    fprintf(stderr, "%s: error: failed to allocate memory for prompt tokens\n",
+            __func__);
+    return 1;
+  }
+  if (llama_tokenize(vocab, prompt, strlen(prompt), prompt_tokens, n_prompt,
+                     true, true) < 0) {
+    fprintf(stderr, "%s: error: failed to tokenize the prompt\n", __func__);
+    return 1;
+  }
+
+  // initialize the context
+
+  struct llama_context_params ctx_params = llama_context_default_params();
+  // n_ctx is the context size
+  ctx_params.n_ctx = n_prompt + n_predict - 1;
+  // n_batch is the maximum number of tokens that can be processed in a single
+  // call to llama_decode
+  ctx_params.n_batch = n_prompt;
+  // enable performance counters
+  ctx_params.no_perf = false;
+
+  struct llama_context *ctx = llama_init_from_model(model, ctx_params);
+
+  if (ctx == NULL) {
+    fprintf(stderr, "%s: error: failed to create the llama_context\n",
+            __func__);
+    return 1;
+  }
+
+  // initialize the sampler
+
+  struct llama_sampler_chain_params sparams =
+      llama_sampler_chain_default_params();
+  sparams.no_perf = false;
+  struct llama_sampler *smpl = llama_sampler_chain_init(sparams);
+
+  llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
+
+  // print the prompt token-by-token
+
+  for (int i = 0; i < n_prompt; i++) {
+    char buf[128];
+    int n = llama_token_to_piece(vocab, prompt_tokens[i], buf, sizeof(buf), 0,
+                                 true);
+    if (n < 0) {
+      fprintf(stderr, "%s: error: failed to convert token to piece\n",
+              __func__);
+      return 1;
+    }
+    buf[n] = '\0';
+    printf("%s", buf);
+  }
+
+  // prepare a batch for the prompt
+
+  llama_batch batch = llama_batch_get_one(prompt_tokens, n_prompt);
+
+  // main loop
+
+  const int64_t t_main_start = ggml_time_us();
+  int n_decode = 0;
+  llama_token new_token_id;
+
+  for (int n_pos = 0; n_pos + batch.n_tokens < n_prompt + n_predict;) {
+    // evaluate the current batch with the transformer model
+    if (llama_decode(ctx, batch)) {
+      fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
+      return 1;
+    }
+
+    n_pos += batch.n_tokens;
+
+    // sample the next token
+    {
+      new_token_id = llama_sampler_sample(smpl, ctx, -1);
+
+      // is it an end of generation?
+      if (llama_vocab_is_eog(vocab, new_token_id)) {
+        break;
+      }
+
+      char buf[128];
+      int n =
+          llama_token_to_piece(vocab, new_token_id, buf, sizeof(buf), 0, true);
+      if (n < 0) {
+        fprintf(stderr, "%s: error: failed to convert token to piece\n",
+                __func__);
+        return 1;
+      }
+      buf[n] = '\0';
+      printf("%s", buf);
+      fflush(stdout);
+
+      // prepare the next batch with the sampled token
+      batch = llama_batch_get_one(&new_token_id, 1);
+
+      n_decode += 1;
+    }
+  }
+
+  printf("\n");
+
+  const int64_t t_main_end = ggml_time_us();
+
+  fprintf(stderr, "%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
+          __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f,
+          n_decode / ((t_main_end - t_main_start) / 1000000.0f));
+
+  fprintf(stderr, "\n");
+  llama_perf_sampler_print(smpl);
+  llama_perf_context_print(ctx);
+  fprintf(stderr, "\n");
+
+  llama_sampler_free(smpl);
+  llama_free(ctx);
+  llama_model_free(model);
+  free(prompt_tokens);
+}