Kye
10 months ago
parent
780b04925e
commit
fbba87060d
@ -0,0 +1,107 @@
|
||||
# How to Create A Custom Language Model
|
||||
|
||||
When working with advanced language models, there might come a time when you need a custom solution tailored to your specific needs. Inheriting from an `AbstractLLM` in a Python framework allows developers to create custom language model classes with ease. This developer guide will take you through the process step by step.
|
||||
|
||||
### Prerequisites
|
||||
|
||||
Before you begin, ensure that you have:
|
||||
|
||||
- A working knowledge of Python programming.
|
||||
- Basic understanding of object-oriented programming (OOP) in Python.
|
||||
- Familiarity with language models and natural language processing (NLP).
|
||||
- The appropriate Python framework installed, with access to `AbstractLLM`.
|
||||
|
||||
### Step-by-Step Guide
|
||||
|
||||
#### Step 1: Understand `AbstractLLM`
|
||||
|
||||
The `AbstractLLM` is an abstract base class that defines a set of methods and properties which your custom language model (LLM) should implement. Abstract classes in Python are not designed to be instantiated directly but are meant to be subclasses.
|
||||
|
||||
#### Step 2: Create a New Class
|
||||
|
||||
Start by defining a new class that inherits from `AbstractLLM`. This class will implement the required methods defined in the abstract base class.
|
||||
|
||||
```python
|
||||
from swarms import AbstractLLM
|
||||
|
||||
class vLLMLM(AbstractLLM):
|
||||
pass
|
||||
```
|
||||
|
||||
#### Step 3: Initialize Your Class
|
||||
|
||||
Implement the `__init__` method to initialize your custom LLM. You'll want to initialize the base class as well and define any additional parameters for your model.
|
||||
|
||||
```python
|
||||
class vLLMLM(AbstractLLM):
|
||||
def __init__(self, model_name='default_model', tensor_parallel_size=1, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.model_name = model_name
|
||||
self.tensor_parallel_size = tensor_parallel_size
|
||||
# Add any additional initialization here
|
||||
```
|
||||
|
||||
#### Step 4: Implement Required Methods
|
||||
|
||||
Implement the `run` method or any other abstract methods required by `AbstractLLM`. This is where you define how your model processes input and returns output.
|
||||
|
||||
```python
|
||||
class vLLMLM(AbstractLLM):
|
||||
# ... existing code ...
|
||||
|
||||
def run(self, task, *args, **kwargs):
|
||||
# Logic for running your model goes here
|
||||
return "Processed output"
|
||||
```
|
||||
|
||||
#### Step 5: Test Your Model
|
||||
|
||||
Instantiate your custom LLM and test it to ensure that it works as expected.
|
||||
|
||||
```python
|
||||
model = vLLMLM(model_name='my_custom_model', tensor_parallel_size=2)
|
||||
output = model.run("What are the symptoms of COVID-19?")
|
||||
print(output) # Outputs: "Processed output"
|
||||
```
|
||||
|
||||
#### Step 6: Integrate Additional Components
|
||||
|
||||
Depending on the requirements, you might need to integrate additional components such as database connections, parallel computing resources, or custom processing pipelines.
|
||||
|
||||
#### Step 7: Documentation
|
||||
|
||||
Write comprehensive docstrings for your class and its methods. Good documentation is crucial for maintaining the code and for other developers who might use your model.
|
||||
|
||||
```python
|
||||
class vLLMLM(AbstractLLM):
|
||||
"""
|
||||
A custom language model class that extends AbstractLLM.
|
||||
|
||||
... more detailed docstring ...
|
||||
"""
|
||||
# ... existing code ...
|
||||
```
|
||||
|
||||
#### Step 8: Best Practices
|
||||
|
||||
Follow best practices such as error handling, input validation, and resource management to ensure your model is robust and reliable.
|
||||
|
||||
#### Step 9: Packaging Your Model
|
||||
|
||||
Package your custom LLM class into a module or package that can be easily distributed and imported into other projects.
|
||||
|
||||
#### Step 10: Version Control and Collaboration
|
||||
|
||||
Use a version control system like Git to track changes to your model. This makes collaboration easier and helps you keep a history of your work.
|
||||
|
||||
### Conclusion
|
||||
|
||||
By following this guide, you should now have a custom model that extends the `AbstractLLM`. Remember that the key to a successful custom LLM is understanding the base functionalities, implementing necessary changes, and testing thoroughly. Keep iterating and improving based on feedback and performance metrics.
|
||||
|
||||
### Further Reading
|
||||
|
||||
- Official Python documentation on abstract base classes.
|
||||
- In-depth tutorials on object-oriented programming in Python.
|
||||
- Advanced NLP techniques and optimization strategies for language models.
|
||||
|
||||
This guide provides the fundamental steps to create custom models using `AbstractLLM`. For detailed implementation and advanced customization, it's essential to dive deeper into the specific functionalities and capabilities of the language model framework you are using.
|
||||
@ -0,0 +1,89 @@
|
||||
from vllm import LLM
|
||||
from swarms import AbstractLLM, Agent, ChromaDB
|
||||
|
||||
|
||||
# Making an instance of the VLLM class
|
||||
class vLLMLM(AbstractLLM):
|
||||
"""
|
||||
This class represents a variant of the Language Model (LLM) called vLLMLM.
|
||||
It extends the AbstractLLM class and provides additional functionality.
|
||||
|
||||
Args:
|
||||
model_name (str): The name of the LLM model to use. Defaults to "acebook/opt-13b".
|
||||
tensor_parallel_size (int): The size of the tensor parallelism. Defaults to 4.
|
||||
*args: Variable length argument list.
|
||||
**kwargs: Arbitrary keyword arguments.
|
||||
|
||||
Attributes:
|
||||
model_name (str): The name of the LLM model.
|
||||
tensor_parallel_size (int): The size of the tensor parallelism.
|
||||
llm (LLM): An instance of the LLM class.
|
||||
|
||||
Methods:
|
||||
run(task: str, *args, **kwargs): Runs the LLM model to generate output for the given task.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name: str = "acebook/opt-13b",
|
||||
tensor_parallel_size: int = 4,
|
||||
*args,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.model_name = model_name
|
||||
self.tensor_parallel_size = tensor_parallel_size
|
||||
|
||||
self.llm = LLM(
|
||||
model_name=self.model_name,
|
||||
tensor_parallel_size=self.tensor_parallel_size,
|
||||
)
|
||||
|
||||
def run(self, task: str, *args, **kwargs):
|
||||
"""
|
||||
Runs the LLM model to generate output for the given task.
|
||||
|
||||
Args:
|
||||
task (str): The task for which to generate output.
|
||||
*args: Variable length argument list.
|
||||
**kwargs: Arbitrary keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The generated output for the given task.
|
||||
|
||||
"""
|
||||
return self.llm.generate(task)
|
||||
|
||||
|
||||
# Initializing the agent with the vLLMLM instance and other parameters
|
||||
model = vLLMLM(
|
||||
"facebook/opt-13b",
|
||||
tensor_parallel_size=4,
|
||||
)
|
||||
|
||||
# Defining the task
|
||||
task = "What are the symptoms of COVID-19?"
|
||||
|
||||
# Running the agent with the specified task
|
||||
out = model.run(task)
|
||||
|
||||
|
||||
# Integrate Agent
|
||||
agent = Agent(
|
||||
agent_name="Doctor agent",
|
||||
agent_description=(
|
||||
"This agent provides information about COVID-19 symptoms."
|
||||
),
|
||||
llm=model,
|
||||
max_loops="auto",
|
||||
autosave=True,
|
||||
verbose=True,
|
||||
long_term_memory=ChromaDB(
|
||||
metric="cosine",
|
||||
n_results=3,
|
||||
output_dir="results",
|
||||
docs_folder="docs",
|
||||
),
|
||||
stopping_condition="finish",
|
||||
)
|
||||
Loading…
Reference in new issue