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https://github.com/nomic-ai/gpt4all.git
synced 2024-10-01 01:06:10 -04:00
updated bindings code for updated C api
This commit is contained in:
Richard Guo
AT
parent
f0be66a221
commit
ae42805d49
@ -22,7 +22,7 @@ class GPT4All():
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model: Pointer to underlying C model.
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"""
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def __init__(self, model_name: str, model_path: str = None, model_type: str = None, allow_download=True):
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def __init__(self, model_name: str, model_path: str = None, model_type: str = None, allow_download=True):
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"""
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Constructor
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@ -30,20 +30,12 @@ class GPT4All():
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model_name: Name of GPT4All or custom model. Including ".bin" file extension is optional but encouraged.
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model_path: Path to directory containing model file or, if file does not exist, where to download model.
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Default is None, in which case models will be stored in `~/.cache/gpt4all/`.
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model_type: Model architecture to use - currently, options are 'llama', 'gptj', or 'mpt'. Only required if model
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is custom. Note that these models still must be built from llama.cpp or GPTJ ggml architecture.
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Default is None.
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model_type: Model architecture. This argument currently does not have any functionality and is just used as
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descriptive identifier for user. Default is None.
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allow_download: Allow API to download models from gpt4all.io. Default is True.
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"""
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self.model = None
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# Model type provided for when model is custom
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if model_type:
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self.model = GPT4All.get_model_from_type(model_type)
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# Else get model from gpt4all model filenames
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else:
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self.model = GPT4All.get_model_from_name(model_name)
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self.model_type = model_type
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self.model = pyllmodel.LLModel()
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# Retrieve model and download if allowed
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model_dest = self.retrieve_model(model_name, model_path=model_path, allow_download=allow_download)
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self.model.load_model(model_dest)
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@ -265,61 +257,6 @@ class GPT4All():
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return full_prompt
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@staticmethod
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def get_model_from_type(model_type: str) -> pyllmodel.LLModel:
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# This needs to be updated for each new model type
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# TODO: Might be worth converting model_type to enum
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if model_type == "gptj":
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return pyllmodel.GPTJModel()
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elif model_type == "llama":
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return pyllmodel.LlamaModel()
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elif model_type == "mpt":
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return pyllmodel.MPTModel()
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else:
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raise ValueError(f"No corresponding model for model_type: {model_type}")
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@staticmethod
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def get_model_from_name(model_name: str) -> pyllmodel.LLModel:
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# This needs to be updated for each new model
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# NOTE: We are doing this preprocessing a lot, maybe there's a better way to organize
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model_name = append_bin_suffix_if_missing(model_name)
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GPTJ_MODELS = [
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"ggml-gpt4all-j-v1.3-groovy.bin",
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"ggml-gpt4all-j-v1.2-jazzy.bin",
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"ggml-gpt4all-j-v1.1-breezy.bin",
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"ggml-gpt4all-j.bin"
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]
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LLAMA_MODELS = [
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"ggml-gpt4all-l13b-snoozy.bin",
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"ggml-vicuna-7b-1.1-q4_2.bin",
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"ggml-vicuna-13b-1.1-q4_2.bin",
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"ggml-wizardLM-7B.q4_2.bin",
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"ggml-stable-vicuna-13B.q4_2.bin",
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"ggml-nous-gpt4-vicuna-13b.bin"
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]
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MPT_MODELS = [
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"ggml-mpt-7b-base.bin",
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"ggml-mpt-7b-chat.bin",
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"ggml-mpt-7b-instruct.bin"
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]
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if model_name in GPTJ_MODELS:
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return pyllmodel.GPTJModel()
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elif model_name in LLAMA_MODELS:
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return pyllmodel.LlamaModel()
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elif model_name in MPT_MODELS:
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return pyllmodel.MPTModel()
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err_msg = (f"No corresponding model for provided filename {model_name}.\n"
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f"If this is a custom model, make sure to specify a valid model_type.\n")
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raise ValueError(err_msg)
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def append_bin_suffix_if_missing(model_name):
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if not model_name.endswith(".bin"):
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@ -54,19 +54,9 @@ def load_llmodel_library():
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llmodel, llama = load_llmodel_library()
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# Define C function signatures using ctypes
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llmodel.llmodel_gptj_create.restype = ctypes.c_void_p
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llmodel.llmodel_gptj_destroy.argtypes = [ctypes.c_void_p]
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llmodel.llmodel_llama_create.restype = ctypes.c_void_p
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llmodel.llmodel_llama_destroy.argtypes = [ctypes.c_void_p]
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llmodel.llmodel_mpt_create.restype = ctypes.c_void_p
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llmodel.llmodel_mpt_destroy.argtypes = [ctypes.c_void_p]
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llmodel.llmodel_loadModel.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
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llmodel.llmodel_loadModel.restype = ctypes.c_bool
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llmodel.llmodel_isModelLoaded.argtypes = [ctypes.c_void_p]
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llmodel.llmodel_isModelLoaded.restype = ctypes.c_bool
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class LLModelError(ctypes.Structure):
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_fields_ = [("message", ctypes.c_char_p),
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("code", ctypes.c_int32)]
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class LLModelPromptContext(ctypes.Structure):
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_fields_ = [("logits", ctypes.POINTER(ctypes.c_float)),
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@ -83,7 +73,17 @@ class LLModelPromptContext(ctypes.Structure):
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("repeat_penalty", ctypes.c_float),
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("repeat_last_n", ctypes.c_int32),
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("context_erase", ctypes.c_float)]
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# Define C function signatures using ctypes
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llmodel.llmodel_model_create2.argtypes = [ctypes.c_char_p, ctypes.c_char_p, ctypes.POINTER(LLModelError)]
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llmodel.llmodel_model_create2.restype = ctypes.c_void_p
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llmodel.llmodel_loadModel.argtypes = [ctypes.c_void_p, ctypes.c_char_p]
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llmodel.llmodel_loadModel.restype = ctypes.c_bool
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llmodel.llmodel_isModelLoaded.argtypes = [ctypes.c_void_p]
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llmodel.llmodel_isModelLoaded.restype = ctypes.c_bool
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PromptCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_int32)
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ResponseCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_int32, ctypes.c_char_p)
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RecalculateCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_bool)
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@ -113,18 +113,17 @@ class LLModel:
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----------
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model: llmodel_model
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Ctype pointer to underlying model
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model_type : str
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Model architecture identifier
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model_name: str
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Model name.
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"""
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model_type: str = None
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def __init__(self):
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self.model = None
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self.model_name = None
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def __del__(self):
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pass
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if self.model is not None and llmodel is not None:
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llmodel.llmodel_model_destroy(self.model)
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def load_model(self, model_path: str) -> bool:
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"""
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@ -139,7 +138,10 @@ class LLModel:
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-------
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True if model loaded successfully, False otherwise
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"""
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llmodel.llmodel_loadModel(self.model, model_path.encode('utf-8'))
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model_path_enc = model_path.encode("utf-8")
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build_var = "auto".encode("utf-8")
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self.model = llmodel.llmodel_model_create2(model_path_enc, build_var, None)
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llmodel.llmodel_loadModel(self.model, model_path_enc)
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filename = os.path.basename(model_path)
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self.model_name = os.path.splitext(filename)[0]
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@ -148,7 +150,6 @@ class LLModel:
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else:
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return False
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def set_thread_count(self, n_threads):
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if not llmodel.llmodel_isModelLoaded(self.model):
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raise Exception("Model not loaded")
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@ -159,7 +160,6 @@ class LLModel:
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raise Exception("Model not loaded")
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return llmodel.llmodel_threadCount(self.model)
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def generate(self,
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prompt: str,
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logits_size: int = 0,
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@ -246,45 +246,3 @@ class LLModel:
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@staticmethod
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def _recalculate_callback(is_recalculating):
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return is_recalculating
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class GPTJModel(LLModel):
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model_type = "gptj"
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def __init__(self):
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super().__init__()
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self.model = llmodel.llmodel_gptj_create()
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def __del__(self):
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if self.model is not None and llmodel is not None:
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llmodel.llmodel_gptj_destroy(self.model)
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super().__del__()
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class LlamaModel(LLModel):
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model_type = "llama"
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def __init__(self):
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super().__init__()
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self.model = llmodel.llmodel_llama_create()
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def __del__(self):
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if self.model is not None and llmodel is not None:
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llmodel.llmodel_llama_destroy(self.model)
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super().__del__()
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class MPTModel(LLModel):
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model_type = "mpt"
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def __init__(self):
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super().__init__()
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self.model = llmodel.llmodel_mpt_create()
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def __del__(self):
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if self.model is not None and llmodel is not None:
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llmodel.llmodel_mpt_destroy(self.model)
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super().__del__()
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