vulkan support for typescript bindings, gguf support (nomic-ai#1390)

* adding some native methods to cpp wrapper * gpu seems to work * typings and add availibleGpus method * fix spelling * fix syntax * more * normalize methods to conform to py * remove extra dynamic linker deps when building with vulkan * bump python version (library linking fix) * Don't link against libvulkan. * vulkan python bindings on windows fixes * Bring the vulkan backend to the GUI. * When device is Auto (the default) then we will only consider discrete GPU's otherwise fallback to CPU. * Show the device we're currently using. * Fix up the name and formatting. * init at most one vulkan device, submodule update fixes issues w/ multiple of the same gpu * Update the submodule. * Add version 2.4.15 and bump the version number. * Fix a bug where we're not properly falling back to CPU. * Sync to a newer version of llama.cpp with bugfix for vulkan. * Report the actual device we're using. * Only show GPU when we're actually using it. * Bump to new llama with new bugfix. * Release notes for v2.4.16 and bump the version. * Fallback to CPU more robustly. * Release notes for v2.4.17 and bump the version. * Bump the Python version to python-v1.0.12 to restrict the quants that vulkan recognizes. * Link against ggml in bin so we can get the available devices without loading a model. * Send actual and requested device info for those who have opt-in. * Actually bump the version. * Release notes for v2.4.18 and bump the version. * Fix for crashes on systems where vulkan is not installed properly. * Release notes for v2.4.19 and bump the version. * fix typings and vulkan build works on win * Add flatpak manifest * Remove unnecessary stuffs from manifest * Update to 2.4.19 * appdata: update software description * Latest rebase on llama.cpp with gguf support. * macos build fixes * llamamodel: metal supports all quantization types now * gpt4all.py: GGUF * pyllmodel: print specific error message * backend: port BERT to GGUF * backend: port MPT to GGUF * backend: port Replit to GGUF * backend: use gguf branch of llama.cpp-mainline * backend: use llamamodel.cpp for StarCoder * conversion scripts: cleanup * convert scripts: load model as late as possible * convert_mpt_hf_to_gguf.py: better tokenizer decoding * backend: use llamamodel.cpp for Falcon * convert scripts: make them directly executable * fix references to removed model types * modellist: fix the system prompt * backend: port GPT-J to GGUF * gpt-j: update inference to match latest llama.cpp insights - Use F16 KV cache - Store transposed V in the cache - Avoid unnecessary Q copy Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> ggml upstream commit 0265f0813492602fec0e1159fe61de1bf0ccaf78 * chatllm: grammar fix * convert scripts: use bytes_to_unicode from transformers * convert scripts: make gptj script executable * convert scripts: add feed-forward length for better compatiblilty This GGUF key is used by all llama.cpp models with upstream support. * gptj: remove unused variables * Refactor for subgroups on mat * vec kernel. * Add q6_k kernels for vulkan. * python binding: print debug message to stderr * Fix regenerate button to be deterministic and bump the llama version to latest we have for gguf. * Bump to the latest fixes for vulkan in llama. * llamamodel: fix static vector in LLamaModel::endTokens * Switch to new models2.json for new gguf release and bump our version to 2.5.0. * Bump to latest llama/gguf branch. * chat: report reason for fallback to CPU * chat: make sure to clear fallback reason on success * more accurate fallback descriptions * differentiate between init failure and unsupported models * backend: do not use Vulkan with non-LLaMA models * Add q8_0 kernels to kompute shaders and bump to latest llama/gguf. * backend: fix build with Visual Studio generator Use the $<CONFIG> generator expression instead of CMAKE_BUILD_TYPE. This is needed because Visual Studio is a multi-configuration generator, so we do not know what the build type will be until `cmake --build` is called. Fixes nomic-ai#1470 * remove old llama.cpp submodules * Reorder and refresh our models2.json. * rebase on newer llama.cpp * python/embed4all: use gguf model, allow passing kwargs/overriding model * Add starcoder, rift and sbert to our models2.json. * Push a new version number for llmodel backend now that it is based on gguf. * fix stray comma in models2.json Signed-off-by: Aaron Miller <apage43@ninjawhale.com> * Speculative fix for build on mac. * chat: clearer CPU fallback messages * Fix crasher with an empty string for prompt template. * Update the language here to avoid misunderstanding. * added EM German Mistral Model * make codespell happy * issue template: remove "Related Components" section * cmake: install the GPT-J plugin (nomic-ai#1487) * Do not delete saved chats if we fail to serialize properly. * Restore state from text if necessary. * Another codespell attempted fix. * llmodel: do not call magic_match unless build variant is correct (nomic-ai#1488) * chatllm: do not write uninitialized data to stream (nomic-ai#1486) * mat*mat for q4_0, q8_0 * do not process prompts on gpu yet * python: support Path in GPT4All.__init__ (nomic-ai#1462) * llmodel: print an error if the CPU does not support AVX (nomic-ai#1499) * python bindings should be quiet by default * disable llama.cpp logging unless GPT4ALL_VERBOSE_LLAMACPP envvar is nonempty * make verbose flag for retrieve_model default false (but also be overridable via gpt4all constructor) should be able to run a basic test: ```python import gpt4all model = gpt4all.GPT4All('/Users/aaron/Downloads/rift-coder-v0-7b-q4_0.gguf') print(model.generate('def fib(n):')) ``` and see no non-model output when successful * python: always check status code of HTTP responses (nomic-ai#1502) * Always save chats to disk, but save them as text by default. This also changes the UI behavior to always open a 'New Chat' and setting it as current instead of setting a restored chat as current. This improves usability by not requiring the user to wait if they want to immediately start chatting. * Update README.md Signed-off-by: umarmnaq <102142660+umarmnaq@users.noreply.github.com> * fix embed4all filename https://discordapp.com/channels/1076964370942267462/1093558720690143283/1161778216462192692 Signed-off-by: Aaron Miller <apage43@ninjawhale.com> * Improves Java API signatures maintaining back compatibility * python: replace deprecated pkg_resources with importlib (nomic-ai#1505) * Updated chat wishlist (nomic-ai#1351) * q6k, q4_1 mat*mat * update mini-orca 3b to gguf2, license Signed-off-by: Aaron Miller <apage43@ninjawhale.com> * convert scripts: fix AutoConfig typo (nomic-ai#1512) * publish config https://docs.npmjs.com/cli/v9/configuring-npm/package-json#publishconfig (nomic-ai#1375) merge into my branch * fix appendBin * fix gpu not initializing first * sync up * progress, still wip on destructor * some detection work * untested dispose method * add js side of dispose * Update gpt4all-bindings/typescript/index.cc Co-authored-by: cebtenzzre <cebtenzzre@gmail.com> Signed-off-by: Jacob Nguyen <76754747+jacoobes@users.noreply.github.com> * Update gpt4all-bindings/typescript/index.cc Co-authored-by: cebtenzzre <cebtenzzre@gmail.com> Signed-off-by: Jacob Nguyen <76754747+jacoobes@users.noreply.github.com> * Update gpt4all-bindings/typescript/index.cc Co-authored-by: cebtenzzre <cebtenzzre@gmail.com> Signed-off-by: Jacob Nguyen <76754747+jacoobes@users.noreply.github.com> * Update gpt4all-bindings/typescript/src/gpt4all.d.ts Co-authored-by: cebtenzzre <cebtenzzre@gmail.com> Signed-off-by: Jacob Nguyen <76754747+jacoobes@users.noreply.github.com> * Update gpt4all-bindings/typescript/src/gpt4all.js Co-authored-by: cebtenzzre <cebtenzzre@gmail.com> Signed-off-by: Jacob Nguyen <76754747+jacoobes@users.noreply.github.com> * Update gpt4all-bindings/typescript/src/util.js Co-authored-by: cebtenzzre <cebtenzzre@gmail.com> Signed-off-by: Jacob Nguyen <76754747+jacoobes@users.noreply.github.com> * fix tests * fix circleci for nodejs * bump version --------- Signed-off-by: Aaron Miller <apage43@ninjawhale.com> Signed-off-by: umarmnaq <102142660+umarmnaq@users.noreply.github.com> Signed-off-by: Jacob Nguyen <76754747+jacoobes@users.noreply.github.com> Co-authored-by: Aaron Miller <apage43@ninjawhale.com> Co-authored-by: Adam Treat <treat.adam@gmail.com> Co-authored-by: Akarshan Biswas <akarshan.biswas@gmail.com> Co-authored-by: Cebtenzzre <cebtenzzre@gmail.com> Co-authored-by: Jan Philipp Harries <jpdus@users.noreply.github.com> Co-authored-by: umarmnaq <102142660+umarmnaq@users.noreply.github.com> Co-authored-by: Alex Soto <asotobu@gmail.com> Co-authored-by: niansa/tuxifan <tuxifan@posteo.de>
dpsalvatierra · Nov 12, 2023 · b5457b0 · b5457b0
1 parent 9cd17fe
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diff --git a/.circleci/continue_config.yml b/.circleci/continue_config.yml
@@ -856,6 +856,7 @@ jobs:
       - node/install-packages:
           app-dir: gpt4all-bindings/typescript
           pkg-manager: yarn
+          override-ci-command: yarn install
       - run:
           command: | 
             cd gpt4all-bindings/typescript
@@ -885,6 +886,7 @@ jobs:
       - node/install-packages:
           app-dir: gpt4all-bindings/typescript
           pkg-manager: yarn
+          override-ci-command: yarn install
       - run:
           command: | 
             cd gpt4all-bindings/typescript
@@ -994,7 +996,7 @@ jobs:
           command: |
             cd gpt4all-bindings/typescript
             npm set //registry.npmjs.org/:_authToken=$NPM_TOKEN
-            npm publish --access public --tag alpha
+            npm publish
 
 workflows:
   version: 2

diff --git a/gpt4all-bindings/typescript/.yarnrc.yml b/gpt4all-bindings/typescript/.yarnrc.yml
@@ -0,0 +1 @@
+nodeLinker: node-modules
diff --git a/gpt4all-bindings/typescript/README.md b/gpt4all-bindings/typescript/README.md
@@ -75,15 +75,12 @@ cd gpt4all-bindings/typescript
 ```sh
 yarn
 ```
-
 *   llama.cpp git submodule for gpt4all can be possibly absent. If this is the case, make sure to run in llama.cpp parent directory
 
 ```sh
 git submodule update --init --depth 1 --recursive
 ```
 
-**AS OF NEW BACKEND** to build the backend,
-
 ```sh
 yarn build:backend
 ```

diff --git a/gpt4all-bindings/typescript/index.cc b/gpt4all-bindings/typescript/index.cc
@@ -1,6 +1,5 @@
 #include "index.h"
 
-Napi::FunctionReference NodeModelWrapper::constructor;
 
 Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
     Napi::Function self = DefineClass(env, "LLModel", {
@@ -13,14 +12,64 @@ Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
        InstanceMethod("embed", &NodeModelWrapper::GenerateEmbedding),
        InstanceMethod("threadCount", &NodeModelWrapper::ThreadCount),
        InstanceMethod("getLibraryPath", &NodeModelWrapper::GetLibraryPath),
+       InstanceMethod("initGpuByString", &NodeModelWrapper::InitGpuByString),
+       InstanceMethod("hasGpuDevice", &NodeModelWrapper::HasGpuDevice),
+       InstanceMethod("listGpu", &NodeModelWrapper::GetGpuDevices),
+       InstanceMethod("memoryNeeded", &NodeModelWrapper::GetRequiredMemory),
+       InstanceMethod("dispose", &NodeModelWrapper::Dispose)
     });
     // Keep a static reference to the constructor
     //
-    constructor = Napi::Persistent(self);
-    constructor.SuppressDestruct();
+    Napi::FunctionReference* constructor = new Napi::FunctionReference();
+    *constructor = Napi::Persistent(self);
+    env.SetInstanceData(constructor);
     return self;
+}
+Napi::Value NodeModelWrapper::GetRequiredMemory(const Napi::CallbackInfo& info) 
+{
+    auto env = info.Env();
+    return Napi::Number::New(env, static_cast<uint32_t>( llmodel_required_mem(GetInference(), full_model_path.c_str()) ));
+
+}
+  Napi::Value NodeModelWrapper::GetGpuDevices(const Napi::CallbackInfo& info) 
+  {
+    auto env = info.Env();
+    int num_devices = 0;
+    auto mem_size = llmodel_required_mem(GetInference(), full_model_path.c_str());
+    llmodel_gpu_device* all_devices = llmodel_available_gpu_devices(GetInference(), mem_size, &num_devices);
+    if(all_devices == nullptr) {
+        Napi::Error::New(
+            env, 
+            "Unable to retrieve list of all GPU devices"
+        ).ThrowAsJavaScriptException(); 
+        return env.Undefined();
+    }
+    auto js_array = Napi::Array::New(env, num_devices);
+    for(int i = 0; i < num_devices; ++i) {
+       auto gpu_device = all_devices[i];
+       /* 
+        *
+        * struct llmodel_gpu_device {
+            int index = 0;
+            int type = 0;           // same as VkPhysicalDeviceType
+            size_t heapSize = 0; 
+            const char * name;
+            const char * vendor;
+          };
+        *
+        */
+       Napi::Object js_gpu_device = Napi::Object::New(env);
+        js_gpu_device["index"] = uint32_t(gpu_device.index);
+        js_gpu_device["type"] = uint32_t(gpu_device.type);
+        js_gpu_device["heapSize"] = static_cast<uint32_t>( gpu_device.heapSize );
+        js_gpu_device["name"]= gpu_device.name;
+        js_gpu_device["vendor"] = gpu_device.vendor;
+
+        js_array[i] = js_gpu_device;
+    }
+    return js_array;
   }
- 
+
   Napi::Value NodeModelWrapper::getType(const Napi::CallbackInfo& info) 
   {
     if(type.empty()) {
@@ -29,15 +78,41 @@ Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
     return Napi::String::New(info.Env(), type);
   }
 
+  Napi::Value NodeModelWrapper::InitGpuByString(const Napi::CallbackInfo& info) 
+  {
+    auto env = info.Env();
+    uint32_t memory_required = info[0].As<Napi::Number>();
+
+    std::string gpu_device_identifier = info[1].As<Napi::String>();   
+
+    size_t converted_value;
+    if(memory_required <= std::numeric_limits<size_t>::max()) {
+        converted_value = static_cast<size_t>(memory_required);
+    } else {
+         Napi::Error::New(
+            env, 
+            "invalid number for memory size. Exceeded bounds for memory."
+        ).ThrowAsJavaScriptException(); 
+        return env.Undefined();
+    }
+
+    auto result = llmodel_gpu_init_gpu_device_by_string(GetInference(), converted_value, gpu_device_identifier.c_str());
+    return Napi::Boolean::New(env, result);
+  }
+  Napi::Value NodeModelWrapper::HasGpuDevice(const Napi::CallbackInfo& info) 
+  {
+    return Napi::Boolean::New(info.Env(), llmodel_has_gpu_device(GetInference()));
+  }
+
   NodeModelWrapper::NodeModelWrapper(const Napi::CallbackInfo& info) : Napi::ObjectWrap<NodeModelWrapper>(info) 
   {
     auto env = info.Env();
     fs::path model_path;
 
-    std::string full_weight_path;
-    //todo
-    std::string library_path = ".";
-    std::string model_name;
+    std::string full_weight_path,
+                library_path = ".",
+                model_name, 
+                device;
     if(info[0].IsString()) {
         model_path = info[0].As<Napi::String>().Utf8Value();
         full_weight_path = model_path.string();
@@ -56,13 +131,14 @@ Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
         } else {
             library_path = ".";
         }
+        device = config_object.Get("device").As<Napi::String>();
     }
     llmodel_set_implementation_search_path(library_path.c_str());
     llmodel_error e = {
         .message="looks good to me",
         .code=0,
     };
-    inference_ = std::make_shared<llmodel_model>(llmodel_model_create2(full_weight_path.c_str(), "auto", &e));
+    inference_ = llmodel_model_create2(full_weight_path.c_str(), "auto", &e);
     if(e.code != 0) {
        Napi::Error::New(env, e.message).ThrowAsJavaScriptException(); 
        return;
@@ -74,18 +150,45 @@ Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
        Napi::Error::New(env, "Had an issue creating llmodel object, inference is null").ThrowAsJavaScriptException(); 
        return;
     }
+    if(device != "cpu") {
+        size_t mem = llmodel_required_mem(GetInference(), full_weight_path.c_str());
+        if(mem == 0) {
+            std::cout << "WARNING: no memory needed. does this model support gpu?\n";
+        }
+        std::cout << "Initiating GPU\n";
+        std::cout << "Memory required estimation: " << mem << "\n";
+
+        auto success = llmodel_gpu_init_gpu_device_by_string(GetInference(), mem, device.c_str());
+        if(success) {
+            std::cout << "GPU init successfully\n";
+        } else {
+            std::cout << "WARNING: Failed to init GPU\n";
+        }
+    }
 
     auto success = llmodel_loadModel(GetInference(), full_weight_path.c_str());
     if(!success) {
         Napi::Error::New(env, "Failed to load model at given path").ThrowAsJavaScriptException(); 
         return;
     }
+
     name = model_name.empty() ? model_path.filename().string() : model_name;
+    full_model_path = full_weight_path;
   };
-  //NodeModelWrapper::~NodeModelWrapper() {
-    //GetInference().reset();
-  //}
 
+//  NodeModelWrapper::~NodeModelWrapper() {
+//    if(GetInference() != nullptr) {
+//        std::cout << "Debug: deleting model\n";
+//        llmodel_model_destroy(inference_);
+//        std::cout << (inference_ == nullptr);
+//    }
+//  }
+//  void NodeModelWrapper::Finalize(Napi::Env env) {
+//    if(inference_ != nullptr) {
+//        std::cout << "Debug: deleting model\n";
+//
+//    } 
+//  }
   Napi::Value NodeModelWrapper::IsModelLoaded(const Napi::CallbackInfo& info) {
     return Napi::Boolean::New(info.Env(), llmodel_isModelLoaded(GetInference()));
   }
@@ -193,8 +296,9 @@ Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
     std::string copiedQuestion = question;
     PromptWorkContext pc = {
         copiedQuestion,
-        std::ref(inference_),
+        inference_,
         copiedPrompt,
+        ""
     };
     auto threadSafeContext = new TsfnContext(env, pc);
     threadSafeContext->tsfn = Napi::ThreadSafeFunction::New(
@@ -210,7 +314,9 @@ Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
     threadSafeContext->nativeThread = std::thread(threadEntry, threadSafeContext);
     return threadSafeContext->deferred_.Promise();
   }
-
+  void NodeModelWrapper::Dispose(const Napi::CallbackInfo& info) {
+    llmodel_model_destroy(inference_);
+  }
   void NodeModelWrapper::SetThreadCount(const Napi::CallbackInfo& info) {
     if(info[0].IsNumber()) {
         llmodel_setThreadCount(GetInference(), info[0].As<Napi::Number>().Int64Value());
@@ -233,7 +339,7 @@ Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
   }
 
   llmodel_model NodeModelWrapper::GetInference() {
-    return *inference_;
+    return inference_;
   }
 
 //Exports Bindings

diff --git a/gpt4all-bindings/typescript/index.h b/gpt4all-bindings/typescript/index.h
@@ -6,24 +6,33 @@
 #include <atomic>
 #include <memory>
 #include <filesystem>
+#include <set>
 namespace fs = std::filesystem;
 
+
 class NodeModelWrapper: public Napi::ObjectWrap<NodeModelWrapper> {
 public:
   NodeModelWrapper(const Napi::CallbackInfo &);
-  //~NodeModelWrapper();
+  //virtual ~NodeModelWrapper();
   Napi::Value getType(const Napi::CallbackInfo& info);
   Napi::Value IsModelLoaded(const Napi::CallbackInfo& info);
   Napi::Value StateSize(const Napi::CallbackInfo& info);
+  //void Finalize(Napi::Env env) override;
   /**
    * Prompting the model. This entails spawning a new thread and adding the response tokens
    * into a thread local string variable.
    */
   Napi::Value Prompt(const Napi::CallbackInfo& info);
   void SetThreadCount(const Napi::CallbackInfo& info);
+  void Dispose(const Napi::CallbackInfo& info);
   Napi::Value getName(const Napi::CallbackInfo& info);
   Napi::Value ThreadCount(const Napi::CallbackInfo& info);
   Napi::Value GenerateEmbedding(const Napi::CallbackInfo& info);
+  Napi::Value HasGpuDevice(const Napi::CallbackInfo& info);
+  Napi::Value ListGpus(const Napi::CallbackInfo& info);
+  Napi::Value InitGpuByString(const Napi::CallbackInfo& info);
+  Napi::Value GetRequiredMemory(const Napi::CallbackInfo& info);
+  Napi::Value GetGpuDevices(const Napi::CallbackInfo& info);
   /*
    * The path that is used to search for the dynamic libraries
    */
@@ -37,10 +46,10 @@ class NodeModelWrapper: public Napi::ObjectWrap<NodeModelWrapper> {
   /**
    * The underlying inference that interfaces with the C interface
    */
-  std::shared_ptr<llmodel_model> inference_;
+  llmodel_model inference_;
 
   std::string type;
   // corresponds to LLModel::name() in typescript
   std::string name;
-  static Napi::FunctionReference constructor;
+  std::string full_model_path;
 };
diff --git a/gpt4all-bindings/typescript/package.json b/gpt4all-bindings/typescript/package.json
@@ -1,6 +1,6 @@
 {
   "name": "gpt4all",
-  "version": "2.2.0",
+  "version": "3.0.0",
   "packageManager": "yarn@3.6.1",
   "main": "src/gpt4all.js",
   "repository": "nomic-ai/gpt4all",
@@ -47,5 +47,10 @@
   },
   "jest": {
     "verbose": true
+  }, 
+  "publishConfig": {
+    "registry": "https://registry.npmjs.org/",
+    "access": "public",
+    "tag": "latest"
   }
 }
diff --git a/gpt4all-bindings/typescript/prompt.cc b/gpt4all-bindings/typescript/prompt.cc
@@ -30,7 +30,7 @@ void threadEntry(TsfnContext* context) {
     context->tsfn.BlockingCall(&context->pc,
     [](Napi::Env env, Napi::Function jsCallback, PromptWorkContext* pc) {
         llmodel_prompt(
-            *pc->inference_,
+            pc->inference_,
             pc->question.c_str(),
             &prompt_callback,
             &response_callback,

diff --git a/gpt4all-bindings/typescript/prompt.h b/gpt4all-bindings/typescript/prompt.h
@@ -10,7 +10,7 @@
 #include <memory>
 struct PromptWorkContext {
     std::string question;
-    std::shared_ptr<llmodel_model>& inference_;
+    llmodel_model inference_;
     llmodel_prompt_context prompt_params;
     std::string res;
 

diff --git a/gpt4all-bindings/typescript/spec/chat.mjs b/gpt4all-bindings/typescript/spec/chat.mjs
@@ -1,8 +1,8 @@
 import { LLModel, createCompletion, DEFAULT_DIRECTORY, DEFAULT_LIBRARIES_DIRECTORY, loadModel } from '../src/gpt4all.js'
 
 const model = await loadModel(
-    'orca-mini-3b-gguf2-q4_0.gguf',
-    { verbose: true }
+    'mistral-7b-openorca.Q4_0.gguf',
+    { verbose: true, device: 'gpu' }
 );
 const ll = model.llm;
 
@@ -26,7 +26,9 @@ console.log("name " + ll.name());
 console.log("type: " + ll.type());
 console.log("Default directory for models", DEFAULT_DIRECTORY);
 console.log("Default directory for libraries", DEFAULT_LIBRARIES_DIRECTORY);
-
+console.log("Has GPU", ll.hasGpuDevice());
+console.log("gpu devices", ll.listGpu())
+console.log("Required Mem in bytes", ll.memoryNeeded())
 const completion1 = await createCompletion(model, [ 
     { role : 'system', content: 'You are an advanced mathematician.'  },
     { role : 'user', content: 'What is 1 + 1?'  }, 
@@ -40,23 +42,25 @@ const completion2 = await createCompletion(model, [
 
 console.log(completion2.choices[0].message)
 
+//CALLING DISPOSE WILL INVALID THE NATIVE MODEL. USE THIS TO CLEANUP
+model.dispose()
 // At the moment, from testing this code, concurrent model prompting is not possible. 
 // Behavior: The last prompt gets answered, but the rest are cancelled
 // my experience with threading is not the best, so if anyone who is good is willing to give this a shot,
 // maybe this is possible
 // INFO: threading with llama.cpp is not the best maybe not even possible, so this will be left here as reference
 
 //const responses = await Promise.all([
-//    createCompletion(ll, [ 
+//    createCompletion(model, [ 
 //    { role : 'system', content: 'You are an advanced mathematician.'  },
 //    { role : 'user', content: 'What is 1 + 1?'  }, 
 //    ], { verbose: true }),
-//    createCompletion(ll, [ 
+//    createCompletion(model, [ 
 //    { role : 'system', content: 'You are an advanced mathematician.'  },
 //    { role : 'user', content: 'What is 1 + 1?'  }, 
 //    ], { verbose: true }),
 //
-//createCompletion(ll, [ 
+//createCompletion(model, [ 
 //    { role : 'system', content: 'You are an advanced mathematician.'  },
 //    { role : 'user', content: 'What is 1 + 1?'  }, 
 //], { verbose: true })