If you’re interested in customizing an agent for a specific task, one way to do this is to fine-tune the models on your dataset.
For preparing dataset you can see this article.
1. Curate the Dataset
- Using NeMo Curator:
- Install NVIDIA NeMo: `pip install nemo_toolkit`
- Use NeMo Curator to prepare your dataset according to your specific requirements.
2. Fine-Tune the Model
- Using NeMo Framework:
1. Setup NeMo:
```python
import nemo
import nemo.collections.nlp as nemo_nlp
```
2. Prepare the Data:
```python
# Example to prepare dataset
from nemo.collections.nlp.data.text_to_text import TextToTextDataset
dataset = TextToTextDataset(file_path="path_to_your_dataset")
```
3. Fine-Tune the Model:
```python
model = nemo_nlp.models.NLPModel.from_pretrained("pretrained_model_name")
model.train(dataset)
model.save_to("path_to_save_fine_tuned_model")
```
- Using HuggingFace Transformers:
1. Install Transformers:
```sh
pip install transformers
```
2. Load Pretrained Model:
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, Trainer, TrainingArguments
model_name = "pretrained_model_name"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
3. Prepare the Data:
```python
from datasets import load_dataset
dataset = load_dataset("path_to_your_dataset")
tokenized_dataset = dataset.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True)
```
4. Fine-Tune the Model:
```python
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=3,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset['train'],
eval_dataset=tokenized_dataset['validation']
)
trainer.train()
model.save_pretrained("path_to_save_fine_tuned_model")
tokenizer.save_pretrained("path_to_save_tokenizer")
```
3. Develop an Agent with LangChain
1. Install LangChain:
```sh
pip install langchain
```
2. Load the Fine-Tuned Model:
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from langchain.llms import HuggingFaceLLM
model = AutoModelForSeq2SeqLM.from_pretrained("path_to_save_fine_tuned_model")
tokenizer = AutoTokenizer.from_pretrained("path_to_save_tokenizer")
llm = HuggingFaceLLM(model=model, tokenizer=tokenizer)
```
3. Define the Agent:
```python
from langchain.agents import Agent
agent = Agent(
llm=llm,
tools=["tool1", "tool2"], # Specify the tools your agent will use
memory="memory_option", # Specify memory options if any
)
```
4. Use the Agent:
```python
response = agent("Your prompt here")
print(response)
```
This process guides you through curating the dataset, fine-tuning the model, and integrating it into the LangChain framework to develop a custom agent.
You can get more details guide links following.
https://huggingface.co/docs/transformers/en/training
https://github.com/NVIDIA/NeMo-Curator/tree/main/examples
https://docs.smith.langchain.com/old/cookbook/fine-tuning-examples
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