Top Open Source LLMs on Hugging Face
Open-source large language models (LLMs) have gained significant traction due to their accessibility and versatility. Hugging Face serves as a leading platform for these models, providing developers and researchers with a rich library to explore. Here’s a look at some of the top open-source LLMs available on Hugging Face, including their pros and cons, performance scores compared to GPT-4, and links for further exploration.
1. GPT-Neo
- Link: GPT-Neo on Hugging Face
Pros:
- Based on the successful GPT-3 architecture.
- Capable of generating coherent and contextually relevant text.
- Available in various sizes (from 125 million to 2.7 billion parameters) for different computational needs.
Cons:
- Training data specifics are not publicly disclosed, raising concerns about bias.
- Performance may vary significantly based on the model size chosen.
Performance Score: ⭐⭐⭐⭐ (4/5) – Performs well but slightly less effective than GPT-4 in nuanced understanding tasks.
2. BLOOM
- Link: BLOOM on Hugging Face
Pros:
- Developed by a collaborative effort of over 1,000 researchers.
- Supports 46 natural languages and 13 programming languages.
- Efficient training techniques enhance its applicability in various NLP tasks.
Cons:
- The model’s complexity may require substantial computational resources.
- Performance can be inconsistent across different languages.
Performance Score: ⭐⭐⭐⭐ (4/5) – Comparable to GPT-4, especially in multilingual tasks.
3. DistilGPT
Pros:
- A distilled version of GPT, offering a balance of performance and efficiency.
- Reduced computational requirements make it suitable for smaller applications.
Cons:
- Lower parameter count (82 million) may limit its performance in complex tasks.
- Less robust in generating long-form content compared to larger models.
Performance Score: ⭐⭐⭐ (3/5) – Effective for basic tasks but falls short against GPT-4 in complexity.
4. TinyLlama-1.1B
Pros:
- Emphasizes efficiency and agility, making it suitable for smaller-scale applications.
- Open-source nature fosters continuous development and community support.
Cons:
- Smaller size may limit versatility and knowledge breadth.
- Potential biases in training data can affect outputs.
Performance Score: ⭐⭐⭐ (3/5) – Good for lightweight applications but lacks the depth of GPT-4.
5. Phi-2
- Link: Phi-2 on Hugging Face
Pros:
- Innovative architecture that challenges traditional LLM norms.
- Trained on a diverse dataset, enhancing its adaptability.
Cons:
- As a newer model, it may still be undergoing optimizations.
- Limited community resources compared to more established models.
Performance Score: ⭐⭐⭐⭐ (4/5) – Competitive with GPT-4, especially in creative tasks.
Conclusion
Open-source LLMs on Hugging Face provide a diverse range of options for developers and researchers looking to harness the power of natural language processing. Each model has its strengths and weaknesses, making them suitable for different applications. As the landscape of AI continues to evolve, these models will play a crucial role in democratizing access to advanced language technologies.
Citations:
[1] https://bitrock.it/blog/open-source-large-language-models-on-hugging-face.html
[2] https://www.youtube.com/watch?v=jlwbqVNBveI
[3] https://obzovu.ru/mdodx/best-open-source-llm-huggingface.html
[4] https://www.linkedin.com/pulse/hugging-face-new-github-llms-arvind-bhardwaj-ab-
[5] https://codewatchers.com/en/list/best-large-language-models-available-on-huggingface-in-2024