How to Use large language models from the Research and Development Center for Large Language Models

This article serves as your guide to using the advanced language models developed by the Research and Development Center for Large Language Models at the National Institute of Informatics. We’ll delve into the models available, their libraries, and a simple implementation process.

Understanding the Models

The repository provides several large language models, specifically designed for text generation tasks. Notable model variants include:

• llm-jp-3-1.8b
• llm-jp-3-3.7b
• llm-jp-3-1.8b-instruct
• llm-jp-3-3.7b-instruct
• llm-jp-3-13b
• llm-jp-3-13b-instruct

These models can leverage diverse applications, making them essential tools in natural language processing tasks.

Required Libraries

To effectively use these models, you will need to install the following libraries:

• torch = 2.3.0
• transformers = 4.40.1
• tokenizers = 0.19.1
• accelerate = 0.29.3
• flash-attn = 2.5.8

Implementation Guide

Using the models can be likened to creating a recipe. You need ingredients (libraries, in this case) and a step-by-step method to get the final dish (text output). Here’s a simple implementation:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('llm-jp/llm-jp-3-3.7b')
model = AutoModelForCausalLM.from_pretrained('llm-jp/llm-jp-3-3.7b', device_map='auto', torch_dtype=torch.bfloat16)

# Prepare input text
text = "Your text here"
tokenized_input = tokenizer.encode(text, add_special_tokens=False, return_tensors='pt').to(model.device)

# Generate output
with torch.no_grad():
    output = model.generate(
        tokenized_input,
        max_new_tokens=100,
        do_sample=True,
        top_p=0.95,
        temperature=0.7,
        repetition_penalty=1.05,
    )[0]

# Decode the generated text
print(tokenizer.decode(output))

In this analogy, loading the model and tokenizer is like preparing your kitchen with necessary utensils and ingredients. Then, encoding the text is akin to measuring out your ingredients. Lastly, generating the output is your skillful cooking, transforming those ingredients into something delicious!

Troubleshooting

While utilizing these language models, you may encounter some common challenges:

• Library Compatibility: Ensure all required libraries are correctly installed and compatible with the model versions.
• Memory Issues: If running on limited hardware, consider using smaller model versions or adjusting the batch size.
• Output Quality: If outputs seem irrelevant, consider fine-tuning the input prompts or adjusting parameters like top_p and temperature.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.