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| import torch | |
| from auto_gptq import AutoGPTQForCausalLM | |
| from huggingface_hub import hf_hub_download | |
| from langchain.llms import LlamaCpp | |
| from transformers import ( | |
| AutoModelForCausalLM, | |
| AutoTokenizer, | |
| LlamaForCausalLM, | |
| LlamaTokenizer, | |
| ) | |
| from constants import CONTEXT_WINDOW_SIZE, MAX_NEW_TOKENS, N_GPU_LAYERS, N_BATCH, MODELS_PATH | |
| def load_quantized_model_gguf_ggml(model_id, model_basename, device_type, logging): | |
| """ | |
| Load a GGUF/GGML quantized model using LlamaCpp. | |
| This function attempts to load a GGUF/GGML quantized model using the LlamaCpp library. | |
| If the model is of type GGML, and newer version of LLAMA-CPP is used which does not support GGML, | |
| it logs a message indicating that LLAMA-CPP has dropped support for GGML. | |
| Parameters: | |
| - model_id (str): The identifier for the model on HuggingFace Hub. | |
| - model_basename (str): The base name of the model file. | |
| - device_type (str): The type of device where the model will run, e.g., 'mps', 'cuda', etc. | |
| - logging (logging.Logger): Logger instance for logging messages. | |
| Returns: | |
| - LlamaCpp: An instance of the LlamaCpp model if successful, otherwise None. | |
| Notes: | |
| - The function uses the `hf_hub_download` function to download the model from the HuggingFace Hub. | |
| - The number of GPU layers is set based on the device type. | |
| """ | |
| try: | |
| logging.info("Using Llamacpp for GGUF/GGML quantized models") | |
| model_path = hf_hub_download( | |
| repo_id=model_id, | |
| filename=model_basename, | |
| resume_download=True, | |
| cache_dir=MODELS_PATH, | |
| ) | |
| kwargs = { | |
| "model_path": model_path, | |
| "n_ctx": CONTEXT_WINDOW_SIZE, | |
| "max_tokens": MAX_NEW_TOKENS, | |
| "n_batch": N_BATCH, # set this based on your GPU & CPU RAM | |
| } | |
| if device_type.lower() == "mps": | |
| kwargs["n_gpu_layers"] = 1 | |
| if device_type.lower() == "cuda": | |
| kwargs["n_gpu_layers"] = N_GPU_LAYERS # set this based on your GPU | |
| return LlamaCpp(**kwargs) | |
| except: | |
| if "ggml" in model_basename: | |
| logging.INFO("If you were using GGML model, LLAMA-CPP Dropped Support, Use GGUF Instead") | |
| return None | |
| def load_quantized_model_qptq(model_id, model_basename, device_type, logging): | |
| """ | |
| Load a GPTQ quantized model using AutoGPTQForCausalLM. | |
| This function loads a quantized model that ends with GPTQ and may have variations | |
| of .no-act.order or .safetensors in their HuggingFace repo. | |
| Parameters: | |
| - model_id (str): The identifier for the model on HuggingFace Hub. | |
| - model_basename (str): The base name of the model file. | |
| - device_type (str): The type of device where the model will run. | |
| - logging (logging.Logger): Logger instance for logging messages. | |
| Returns: | |
| - model (AutoGPTQForCausalLM): The loaded quantized model. | |
| - tokenizer (AutoTokenizer): The tokenizer associated with the model. | |
| Notes: | |
| - The function checks for the ".safetensors" ending in the model_basename and removes it if present. | |
| """ | |
| # The code supports all huggingface models that ends with GPTQ and have some variation | |
| # of .no-act.order or .safetensors in their HF repo. | |
| logging.info("Using AutoGPTQForCausalLM for quantized models") | |
| if ".safetensors" in model_basename: | |
| # Remove the ".safetensors" ending if present | |
| model_basename = model_basename.replace(".safetensors", "") | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) | |
| logging.info("Tokenizer loaded") | |
| model = AutoGPTQForCausalLM.from_quantized( | |
| model_id, | |
| model_basename=model_basename, | |
| use_safetensors=True, | |
| trust_remote_code=True, | |
| device_map="auto", | |
| use_triton=False, | |
| quantize_config=None, | |
| ) | |
| return model, tokenizer | |
| def load_full_model(model_id, model_basename, device_type, logging): | |
| """ | |
| Load a full model using either LlamaTokenizer or AutoModelForCausalLM. | |
| This function loads a full model based on the specified device type. | |
| If the device type is 'mps' or 'cpu', it uses LlamaTokenizer and LlamaForCausalLM. | |
| Otherwise, it uses AutoModelForCausalLM. | |
| Parameters: | |
| - model_id (str): The identifier for the model on HuggingFace Hub. | |
| - model_basename (str): The base name of the model file. | |
| - device_type (str): The type of device where the model will run. | |
| - logging (logging.Logger): Logger instance for logging messages. | |
| Returns: | |
| - model (Union[LlamaForCausalLM, AutoModelForCausalLM]): The loaded model. | |
| - tokenizer (Union[LlamaTokenizer, AutoTokenizer]): The tokenizer associated with the model. | |
| Notes: | |
| - The function uses the `from_pretrained` method to load both the model and the tokenizer. | |
| - Additional settings are provided for NVIDIA GPUs, such as loading in 4-bit and setting the compute dtype. | |
| """ | |
| if device_type.lower() in ["mps", "cpu"]: | |
| logging.info("Using LlamaTokenizer") | |
| tokenizer = LlamaTokenizer.from_pretrained(model_id, cache_dir="./models/") | |
| model = LlamaForCausalLM.from_pretrained(model_id, cache_dir="./models/") | |
| else: | |
| logging.info("Using AutoModelForCausalLM for full models") | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir="./models/") | |
| logging.info("Tokenizer loaded") | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| device_map="auto", | |
| torch_dtype=torch.float16, | |
| low_cpu_mem_usage=True, | |
| cache_dir=MODELS_PATH, | |
| # trust_remote_code=True, # set these if you are using NVIDIA GPU | |
| # load_in_4bit=True, | |
| # bnb_4bit_quant_type="nf4", | |
| # bnb_4bit_compute_dtype=torch.float16, | |
| max_memory={0: "15GB"} # Uncomment this line with you encounter CUDA out of memory errors | |
| ) | |
| model.tie_weights() | |
| return model, tokenizer | |