updated bindings code for updated C api

gpt4all-bindings/python/gpt4all/gpt4all.py

@@ -30,20 +30,12 @@ class GPT4All():
             model_name: Name of GPT4All or custom model. Including ".bin" file extension is optional but encouraged.
             model_path: Path to directory containing model file or, if file does not exist, where to download model.
                 Default is None, in which case models will be stored in `~/.cache/gpt4all/`.
-            model_type: Model architecture to use - currently, options are 'llama', 'gptj', or 'mpt'. Only required if model
+            model_type: Model architecture. This argument currently does not have any functionality and is just used as
-                is custom. Note that these models still must be built from llama.cpp or GPTJ ggml architecture.
+                descriptive identifier for user. Default is None.
-                Default is None.
             allow_download: Allow API to download models from gpt4all.io. Default is True. 
         """
-        self.model = None
+        self.model_type = model_type
-
+        self.model = pyllmodel.LLModel()
-        # Model type provided for when model is custom
-        if model_type:
-            self.model = GPT4All.get_model_from_type(model_type)
-        # Else get model from gpt4all model filenames
-        else:
-            self.model = GPT4All.get_model_from_name(model_name)
-
         # Retrieve model and download if allowed
         model_dest = self.retrieve_model(model_name, model_path=model_path, allow_download=allow_download)
         self.model.load_model(model_dest)
@@ -265,61 +257,6 @@ class GPT4All():
 
         return full_prompt
 
-    @staticmethod
-    def get_model_from_type(model_type: str) -> pyllmodel.LLModel:
-        # This needs to be updated for each new model type
-        # TODO: Might be worth converting model_type to enum
-
-        if model_type == "gptj":
-            return pyllmodel.GPTJModel()
-        elif model_type == "llama":
-            return pyllmodel.LlamaModel()
-        elif model_type == "mpt":
-            return pyllmodel.MPTModel()
-        else:
-            raise ValueError(f"No corresponding model for model_type: {model_type}")
-
-    @staticmethod
-    def get_model_from_name(model_name: str) -> pyllmodel.LLModel:
-        # This needs to be updated for each new model
-
-        # NOTE: We are doing this preprocessing a lot, maybe there's a better way to organize
-        model_name = append_bin_suffix_if_missing(model_name)
-
-        GPTJ_MODELS = [
-            "ggml-gpt4all-j-v1.3-groovy.bin",
-            "ggml-gpt4all-j-v1.2-jazzy.bin",
-            "ggml-gpt4all-j-v1.1-breezy.bin",
-            "ggml-gpt4all-j.bin"
-        ]
-
-        LLAMA_MODELS = [
-            "ggml-gpt4all-l13b-snoozy.bin",
-            "ggml-vicuna-7b-1.1-q4_2.bin",
-            "ggml-vicuna-13b-1.1-q4_2.bin",
-            "ggml-wizardLM-7B.q4_2.bin",
-            "ggml-stable-vicuna-13B.q4_2.bin",
-            "ggml-nous-gpt4-vicuna-13b.bin"
-        ]
-
-        MPT_MODELS = [
-            "ggml-mpt-7b-base.bin",
-            "ggml-mpt-7b-chat.bin",
-            "ggml-mpt-7b-instruct.bin"
-        ]
-
-        if model_name in GPTJ_MODELS:
-            return pyllmodel.GPTJModel()
-        elif model_name in LLAMA_MODELS:
-            return pyllmodel.LlamaModel()
-        elif model_name in MPT_MODELS:
-            return pyllmodel.MPTModel()
-
-        err_msg = (f"No corresponding model for provided filename {model_name}.\n"
-                   f"If this is a custom model, make sure to specify a valid model_type.\n")
-
-        raise ValueError(err_msg)
-
 
 def append_bin_suffix_if_missing(model_name):
     if not model_name.endswith(".bin"):

gpt4all-bindings/python/gpt4all/pyllmodel.py

@@ -54,19 +54,9 @@ def load_llmodel_library():
 
 llmodel, llama = load_llmodel_library()
 
-# Define C function signatures using ctypes
+class LLModelError(ctypes.Structure):
-llmodel.llmodel_gptj_create.restype = ctypes.c_void_p
+    _fields_ = [("message", ctypes.c_char_p),
-llmodel.llmodel_gptj_destroy.argtypes = [ctypes.c_void_p]
+                ("code", ctypes.c_int32)]
-llmodel.llmodel_llama_create.restype = ctypes.c_void_p
-llmodel.llmodel_llama_destroy.argtypes = [ctypes.c_void_p]
-llmodel.llmodel_mpt_create.restype = ctypes.c_void_p
-llmodel.llmodel_mpt_destroy.argtypes = [ctypes.c_void_p]
-
-
-llmodel.llmodel_loadModel.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
-llmodel.llmodel_loadModel.restype = ctypes.c_bool
-llmodel.llmodel_isModelLoaded.argtypes = [ctypes.c_void_p]
-llmodel.llmodel_isModelLoaded.restype = ctypes.c_bool
 
 class LLModelPromptContext(ctypes.Structure):
     _fields_ = [("logits", ctypes.POINTER(ctypes.c_float)),
@@ -84,6 +74,16 @@ class LLModelPromptContext(ctypes.Structure):
                 ("repeat_last_n", ctypes.c_int32),
                 ("context_erase", ctypes.c_float)]
 
+# Define C function signatures using ctypes
+
+llmodel.llmodel_model_create2.argtypes = [ctypes.c_char_p, ctypes.c_char_p, ctypes.POINTER(LLModelError)]
+llmodel.llmodel_model_create2.restype = ctypes.c_void_p
+
+llmodel.llmodel_loadModel.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
+llmodel.llmodel_loadModel.restype = ctypes.c_bool
+llmodel.llmodel_isModelLoaded.argtypes = [ctypes.c_void_p]
+llmodel.llmodel_isModelLoaded.restype = ctypes.c_bool
+
 PromptCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_int32)
 ResponseCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_int32, ctypes.c_char_p)
 RecalculateCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_bool)
@@ -113,18 +113,17 @@ class LLModel:
     ----------
     model: llmodel_model
         Ctype pointer to underlying model
-    model_type : str
+    model_name: str
-        Model architecture identifier
+        Model name.
     """
 
-    model_type: str = None
-
     def __init__(self):
         self.model = None
         self.model_name = None
 
     def __del__(self):
-        pass
+        if self.model is not None and llmodel is not None:
+            llmodel.llmodel_model_destroy(self.model)
 
     def load_model(self, model_path: str) -> bool:
         """
@@ -139,7 +138,10 @@ class LLModel:
         -------
         True if model loaded successfully, False otherwise
         """
-        llmodel.llmodel_loadModel(self.model, model_path.encode('utf-8'))
+        model_path_enc = model_path.encode("utf-8")
+        build_var = "auto".encode("utf-8")
+        self.model = llmodel.llmodel_model_create2(model_path_enc, build_var, None)
+        llmodel.llmodel_loadModel(self.model, model_path_enc)
         filename = os.path.basename(model_path)
         self.model_name = os.path.splitext(filename)[0]
     
@@ -148,7 +150,6 @@ class LLModel:
         else:
             return False
 
-
     def set_thread_count(self, n_threads):
         if not llmodel.llmodel_isModelLoaded(self.model):
             raise Exception("Model not loaded")
@@ -159,7 +160,6 @@ class LLModel:
             raise Exception("Model not loaded")
         return llmodel.llmodel_threadCount(self.model)
 
-
     def generate(self, 
                  prompt: str,
                  logits_size: int = 0, 
@@ -246,45 +246,3 @@ class LLModel:
     @staticmethod
     def _recalculate_callback(is_recalculating):
         return is_recalculating
-
-
-class GPTJModel(LLModel):
-
-    model_type = "gptj"
-
-    def __init__(self):
-        super().__init__()
-        self.model = llmodel.llmodel_gptj_create()
-
-    def __del__(self):
-        if self.model is not None and llmodel is not None:
-            llmodel.llmodel_gptj_destroy(self.model)
-        super().__del__()
-
-
-class LlamaModel(LLModel):
-
-    model_type = "llama"
-
-    def __init__(self):
-        super().__init__()
-        self.model = llmodel.llmodel_llama_create()
-
-    def __del__(self):
-        if self.model is not None and llmodel is not None:
-            llmodel.llmodel_llama_destroy(self.model)
-        super().__del__()
-
-
-class MPTModel(LLModel):
-
-    model_type = "mpt"
-
-    def __init__(self):
-        super().__init__()
-        self.model = llmodel.llmodel_mpt_create()
-
-    def __del__(self):
-        if self.model is not None and llmodel is not None:
-            llmodel.llmodel_mpt_destroy(self.model)
-        super().__del__()