What is Llama?

Llama is a state-of-the-art large language model series from Meta AI (Facebook). With billions of parameters, Llama is designed for enhanced reasoning, coding, and broad application across multiple languages and tasks.

We are living in an extraordinary era where open-source initiatives, powered by passionate communities, stand toe-to-toe with expensive proprietary solutions from tech giants. A prime example of this progress is the rise of compact yet highly effective language models like Vicuna, Koala, Alpaca, and StableLM. These models achieve performance levels comparable to ChatGPT while operating with minimal computational resources. What unifies them is their foundation in Meta AI’s LLaMA models.

For a deeper dive into other notable open-source advancements in language technologies, check out our article on the 12 GPT-4 Open-Source Alternatives.

In this discussion, we will examine Meta AI’s LLaMA models, their capabilities, and how to access them via the transformers library. We will also compare their performance, highlight key challenges, and explore their limitations. Since this article was first written, Meta AI has introduced both LLaMA 2 and LLaMA 3—each of which we cover in dedicated articles with further insights.

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Understanding LLaMA: Meta AI's Large Language Model

LLaMA (Large Language Model Meta AI) is a series of cutting-edge foundational language models ranging in size from 7 billion to 65 billion parameters. Despite their compact nature, these models deliver outstanding performance, reducing the computational demands for researchers and developers to experiment, verify existing work, and explore innovative applications.

These foundational models have been trained on extensive unlabeled datasets, making them highly adaptable for fine-tuning across various tasks. The training data sources include:

67.0% CommonCrawl
15.0% C4
4.5% GitHub
4.5% Wikipedia
4.5% Books
2.5% ArXiv
2.0% StackExchange

Thanks to this diverse dataset, LLaMA models have achieved performance on par with top-tier models such as Chinchilla-70B and PaLM-540B, solidifying their place among the best-performing AI language models available today.

Understanding Meta's LLaMA Model

LLaMA is an auto-regressive language model based on the transformer architecture. Similar to other advanced models, it processes a sequence of words as input and predicts the next word, enabling recursive text generation.

What makes LLaMA unique is its extensive training on publicly available text data across multiple languages, including Bulgarian, Catalan, Czech, Danish, German, English, Spanish, French, Croatian, Hungarian, Italian, Dutch, Polish, Portuguese, Romanian, Russian, Slovenian, Serbian, Swedish, and Ukrainian. With the introduction of LLaMA 2 in 2024, enhancements in architecture and training techniques have further strengthened its efficiency and multilingual proficiency.

Available in different sizes—7B, 13B, 33B, and 65B parameters—LLaMA models can be accessed via Hugging Face (for compatibility with Transformers) or through the official repository at facebookresearch/llama.

Getting Started with LLaMA Models

The official inference code is available in the facebookresearch/llama repository, but to simplify things, we will use the Hugging Face transformers library to load the model and generate text.

1. Install Necessary Libraries

We will run LLaMA inference using Google Colab.


%%capture
%pip install transformers SentencePiece accelerate

2. Load LLaMA Tokens and Model Weights

Note: "decapoda-research/llama-7b-hf" is not the official model weight. Decapoda Research has adapted the original model to work with the Transformers library.


import transformers, torch
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig

tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
model = LlamaForCausalLM.from_pretrained(
        "decapoda-research/llama-7b-hf",
        load_in_8bit=False,
        torch_dtype=torch.float16,
        device_map="auto",
    )

3. Define the Question

4. Convert Text into Tokens

5. Set Model Generation Configuration

6. Generate Text Output

7. Decode and Print the Response


instruction = "What is the speed of light?"
inputs = tokenizer(
    f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction: {instruction}
### Response:""",
    return_tensors="pt",
)
input_ids = inputs["input_ids"].to("cuda")

generation_config = transformers.GenerationConfig(
    do_sample=True,
    temperature=0.1,
    top_p=0.75,
    top_k=80,
    repetition_penalty=1.5,
    max_new_tokens=128,
)

with torch.no_grad():
    generation_output = model.generate(
        input_ids=input_ids,
        attention_mask=torch.ones_like(input_ids),
        generation_config=generation_config,
    )
output_text = tokenizer.decode(
    generation_output[0].cuda(), skip_special_tokens=True
).strip()
print(output_text)

Output:

The model accurately determines that the speed of light in a vacuum is approximately 299,792,458 meters per second.


Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction: What is the speed of light?
### Response: The speed of light in a vacuum is exactly 299,792,458 meters per second (approximately 186,282 miles per second). This value is a fundamental constant in physics and plays a crucial role in theories like relativity. Scientists have verified this speed through numerous experiments over the years.

The LLaMA model, along with the Transformers library, can also be fine-tuned for various tasks and datasets, significantly enhancing accuracy and performance.

How Does LLaMA Stand Out From Other AI Models?

The research paper offers an in-depth comparison of LLaMA models with top-tier language models such as GPT-3, GPT-NeoX, Gopher, Chinchilla, and PaLM. Various benchmark tests were conducted to evaluate their performance in areas including common sense reasoning, trivia, reading comprehension, question answering, mathematical problem-solving, code generation, and domain knowledge.

Common Sense Reasoning

In benchmark tests like PIQA, SIQA, and OpenBookQA, the LLaMA-65B model surpassed other state-of-the-art architectures. Additionally, even the smaller LLaMA-33B model excelled in ARC (both easy and challenging) when compared to its counterparts.

Closed-Book Question Answering & Trivia

Evaluating the model’s ability to interpret and respond to realistic questions, LLaMA consistently outperformed GPT-3, Gopher, Chinchilla, and PaLM in Natural Questions and TriviaQA assessments.

Reading Comprehension

Using RACE-middle and RACE-high benchmark tests, LLaMA demonstrated better performance than GPT-3 and showed results comparable to PaLM 540B.

Mathematical Reasoning

Since LLaMA was not fine-tuned on mathematical data, it performed below expectations in this domain, trailing behind Minerva.

Code Generation

Assessed through HumanEval and MBPP benchmarks, LLaMA achieved higher scores than LAMDA and PaLM in HumanEval@100, MBP@1, and MBP@80.

Domain Knowledge

When it comes to broad domain knowledge, LLaMA models fell short in comparison to the extensive PaLM 540B model, which benefits from a significantly larger number of parameters.

Challenges and Limitations of LLaMA

Like other large language models, LLaMA is prone to hallucinations, sometimes generating inaccurate or misleading information.

Beyond that, several other challenges exist:

Language Limitations: Since the majority of the training data is in English, LLaMA's performance in other languages may not be as strong.
Research-Focused Use: LLaMA is designed primarily for research purposes under a non-commercial license. Its release aims to help researchers address biases, risks, and issues related to toxic content, hallucinations, and model safety.
Not Production-Ready: As a base model, LLaMA should not be used in applications without thorough risk evaluation and mitigation strategies.
Weakness in Mathematical and Domain-Specific Reasoning: The model struggles with complex mathematical problems and lacks deep expertise in specific domains.

For insights into developments in AI, including OpenAI, Google AI, and their impact on data science, check out The Latest On OpenAI, Google AI, and What It Means for Data Science. The blog explores cutting-edge advancements in language, vision, and multimodal technologies that enhance productivity and efficiency.

With the release of LLaMA 2 and LLaMA 3, new limitations have been identified, though improvements have been made, particularly in context length and adaptability through fine-tuning. As research continues, the AI community is actively working to enhance the robustness and real-world usability of these models.

Conclusion

The emergence of LLaMA models has ushered in a transformative era in open-source AI research. Notably, the compact LLaMA-13B model surpasses GPT-3, while the larger LLaMA-65B demonstrates capabilities on par with advanced models such as Chinchilla-70B and PaLM-540B. These breakthroughs underscore the feasibility of achieving top-tier performance using publicly available datasets and minimal computational power.

Furthermore, the study emphasizes the impact of instruction-based fine-tuning in enhancing LLaMA’s performance. Models like Vicuna and Stanford Alpaca, refined through instruction-following datasets, have demonstrated results comparable to ChatGPT and Bard, showcasing the immense potential of this approach.

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