code clean

README.md

@@ -42,18 +42,22 @@ Dolphin employs a decoder-decoder framework with two main components:
 ![Model Architecture](modelstructure.jpg)
 
 ## Running the Model
-Method 1 : download this repository and run the following commands:
+### Method 1
+download this repository and run the following commands:
 ```bash
 git lfs install
 git clone https://huggingface.co/NexaAIDev/Dolphin
 python inference_example.py
 ```
 
-Method 2 : install `dolphin` package
+### Method 2
+Install `dolphin` package
 ```
 pip install nexaai-dolphin
 ```
+
 Then run the following commands:
+
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
 import torch
@@ -75,14 +79,12 @@ def inference_instruct(mycontext, question, device="cuda:0"):
         .unsqueeze(0)
         .to(device)
     )
-    # to process the context
     context_tokenized = tokenizer(
         mycontext + "".join([f"[memory_{i}]" for i in range(MEMORY_SIZE)]),
         return_tensors="pt",
     )
     context_tokenized = {k: v.to(device) for k, v in context_tokenized.items()}
     context_token_count = (context_tokenized["input_ids"]).shape[1] - MEMORY_SIZE
-    # We conduct a inference process
     for i in range(context_token_count):
         next_token = (
             model(
@@ -106,14 +108,12 @@ if __name__ == "__main__":
     device_name = "cuda:0" if torch.cuda.is_available() else "cpu"
     AutoConfig.register("dolphin", DolphinConfig)
     AutoModelForCausalLM.register(DolphinConfig, DolphinForCausalLM)
-    # Load the tokenizer and model
     tokenizer = AutoTokenizer.from_pretrained('NexaAIDev/Dolphin')
     model = AutoModelForCausalLM.from_pretrained('NexaAIDev/Dolphin', trust_remote_code=True, torch_dtype=torch.bfloat16, device_map=device_name)
     
     # Run inference example
     mycontext = "Nexa AI is a Cupertino-based company founded in May 2023 that researches and develops models and tools for on-device AI applications. The company is founded by Alex and Zack. The company is known for its Octopus-series models, which rival large-scale language models in capabilities such as function-calling, multimodality, and action-planning, while remaining efficient and compact for edge device deployment. Nexa AI's mission is to advance on-device AI in collaboration with the global developer community. To this end, the company has created an on-device model hub for users to find, share, and collaborate on open-source AI models optimized for edge devices, as well as an SDK for developers to run and deploy AI models locally"
     question = "Who founded Nexa AI?"
-    # Pass the context and the correct device string
     result = inference_instruct(mycontext, question, device=device_name)
     print("Result:", result)
 ```