feat: add getModelInfo method (#63)

lib/binding.ts

@@ -54,6 +54,7 @@ export type EmbeddingResult = {
 export interface LlamaContext {
   new (options: LlamaModelOptions): LlamaContext
   getSystemInfo(): string
+  getModelInfo(): object
   getFormattedChat(messages: ChatMessage[]): string
   completion(options: LlamaCompletionOptions, callback?: (token: LlamaCompletionToken) => void): Promise<LlamaCompletionResult>
   stopCompletion(): void

src/LlamaContext.cpp

@@ -25,6 +25,9 @@ void LlamaContext::Init(Napi::Env env, Napi::Object &exports) {
       {InstanceMethod<&LlamaContext::GetSystemInfo>(
            "getSystemInfo",
            static_cast<napi_property_attributes>(napi_enumerable)),
+       InstanceMethod<&LlamaContext::GetModelInfo>(
+           "getModelInfo",
+           static_cast<napi_property_attributes>(napi_enumerable)),
        InstanceMethod<&LlamaContext::GetFormattedChat>(
            "getFormattedChat",
            static_cast<napi_property_attributes>(napi_enumerable)),
@@ -102,6 +105,44 @@ Napi::Value LlamaContext::GetSystemInfo(const Napi::CallbackInfo &info) {
   return Napi::String::New(info.Env(), _info);
 }
 
+bool validateModelChatTemplate(const struct llama_model * model) {
+    std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
+    std::string template_key = "tokenizer.chat_template";
+    int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
+    if (res >= 0) {
+        llama_chat_message chat[] = {{"user", "test"}};
+        std::string tmpl = std::string(model_template.data(), model_template.size());
+        int32_t chat_res = llama_chat_apply_template(model, tmpl.c_str(), chat, 1, true, nullptr, 0);
+        return chat_res > 0;
+    }
+    return res > 0;
+}
+
+// getModelInfo(): object
+Napi::Value LlamaContext::GetModelInfo(const Napi::CallbackInfo &info) {
+  char desc[1024];
+  auto model = _sess->model();
+  llama_model_desc(model, desc, sizeof(desc));
+
+  int count = llama_model_meta_count(model);
+  Napi::Object metadata = Napi::Object::New(info.Env());
+  for (int i = 0; i < count; i++) {
+    char key[256];
+    llama_model_meta_key_by_index(model, i, key, sizeof(key));
+    char val[2048];
+    llama_model_meta_val_str_by_index(model, i, val, sizeof(val));
+
+    metadata.Set(key, val);
+  }
+  Napi::Object details = Napi::Object::New(info.Env());
+  details.Set("desc", desc);
+  details.Set("nParams", llama_model_n_params(model));
+  details.Set("size", llama_model_size(model));
+  details.Set("isChatTemplateSupported", validateModelChatTemplate(model));
+  details.Set("metadata", metadata);
+  return details;
+}
+
 // getFormattedChat(messages: [{ role: string, content: string }]): string
 Napi::Value LlamaContext::GetFormattedChat(const Napi::CallbackInfo &info) {
   Napi::Env env = info.Env();

src/LlamaContext.h

@@ -9,6 +9,7 @@ class LlamaContext : public Napi::ObjectWrap<LlamaContext> {
 
 private:
   Napi::Value GetSystemInfo(const Napi::CallbackInfo &info);
+  Napi::Value GetModelInfo(const Napi::CallbackInfo &info);
   Napi::Value GetFormattedChat(const Napi::CallbackInfo &info);
   Napi::Value Completion(const Napi::CallbackInfo &info);
   void StopCompletion(const Napi::CallbackInfo &info);
@@ -20,6 +21,7 @@ class LlamaContext : public Napi::ObjectWrap<LlamaContext> {
   Napi::Value Release(const Napi::CallbackInfo &info);
 
   std::string _info;
+  Napi::Object _meta;
   LlamaSessionPtr _sess = nullptr;
   LlamaCompletionWorker *_wip = nullptr;
 };

test/__snapshots__/index.test.ts.snap

@@ -421,3 +421,29 @@ exports[`work fine 1`] = `
   "truncated": false,
 }
 `;
+
+exports[`work fine: model info 1`] = `
+{
+  "desc": "llama ?B F16",
+  "isChatTemplateSupported": false,
+  "metadata": {
+    "general.architecture": "llama",
+    "general.file_type": "1",
+    "general.name": "LLaMA v2",
+    "llama.attention.head_count": "2",
+    "llama.attention.head_count_kv": "2",
+    "llama.attention.layer_norm_rms_epsilon": "0.000010",
+    "llama.block_count": "1",
+    "llama.context_length": "4096",
+    "llama.embedding_length": "8",
+    "llama.feed_forward_length": "32",
+    "llama.rope.dimension_count": "4",
+    "tokenizer.ggml.bos_token_id": "1",
+    "tokenizer.ggml.eos_token_id": "2",
+    "tokenizer.ggml.model": "llama",
+    "tokenizer.ggml.unknown_token_id": "0",
+  },
+  "nParams": 513048,
+  "size": 1026144,
+}
+`;

test/index.test.ts

@@ -5,6 +5,8 @@ import { loadModel } from '../lib'
 it('work fine', async () => {
   let tokens = ''
   const model = await loadModel({ model: path.resolve(__dirname, './tiny-random-llama.gguf') })
+  const info = model.getModelInfo()
+  expect(info).toMatchSnapshot('model info')
   const result = await model.completion({
     prompt: 'My name is Merve and my favorite',
     n_samples: 1,