Explore the commonly used open-source frameworks and models that can help you build chatbots similar to ChatGPT, incorporate large language models (LLMs), or release your own AI product.
1. LLaMA
The LLaMA project comprises a series of fundamental language models that range in size from 7 billion to 65 billion parameters. These models were trained on publicly available datasets using millions of tokens. As a result, the LLaMA-13B model surpasses the performance of GPT-3 (175B), while the LLaMA-65B model performs similarly to top-performing models like Chinchilla-70B and PaLM-540B.
2. Alpaca
According to Stanford Alpaca, their model can rival ChatGPT, and it can be replicated for under $600 by anyone. The Alpaca 7B model is fine-tuned from the LLaMA 7B model based on 52,000 demonstrations of following instructions.
3. Vicuna
Vicuna is a model that is fine-tuned from the LLaMA model using conversations shared by users via ShareGPT. The Vicuna-13B model has achieved a quality level of over 90%* compared to OpenAI's ChatGPT and Google's Bard. It has also surpassed the LLaMA and Stanford Alpaca models in 90% of cases. Training the Vicuna model cost around $300.
4. OpenChatKit
OpenChatKit is an open-source alternative to ChatGPT that offers a comprehensive toolkit for building your own chatbot. It includes instructions for training an instruction-tuned large language model, fine-tuning the model, an extensible retrieval system for updating bot responses, and bot moderation to filter out inappropriate content.
It is evident that the GPT-NeoXT-Chat-Base-20B model has exceeded the performance of the GPT-NeoX base model in tasks such as question and answer extraction and classification.
5. GPT4ALL
GPT4ALL is a project driven by the community, and its training is based on a massive curated corpus of assistant interactions that includes code, stories, depictions, and multi-turn dialogue. The team has shared datasets, model weights, data curation processes, and training code to encourage open-source development. Additionally, they have released 4-bit quantized versions of the model that can be run on laptops. A Python client is also available for running model inference.
6.Raven RWKV
Raven RWKV 7B is an open-source chatbot that utilizes the RWKV language model to generate outputs comparable to those of ChatGPT. The model employs RNNs that can match transformers in quality and scaling while being faster and requiring less VRAM. Raven was fine-tuned on multiple datasets, including Stanford Alpaca and code-alpaca.
7. OPT
Although not as powerful as ChatGPT, OPT (Open Pre-trained Transformer Language Models) has demonstrated exceptional capabilities in zero- and few-shot learning and stereotypical bias analysis. Moreover, it can be integrated with Alpa, Colossal-AI, CTranslate2, and FasterTransformer to enhance its performance further. Despite this, OPT has gained popularity, with 624,710 monthly downloads in the text generation category.
8. Flan-T5-XXL
Flan-T5-XXL is a set of T5 models fine-tuned on various datasets phrased as instructions, resulting in a significant improvement in performance across different model classes, including PaLM, T5, and U-PaLM. The model is further fine-tuned on over 1000 additional tasks covering multiple languages, enhancing its capabilities even further.
1. LLaMA
The LLaMA project comprises a series of fundamental language models that range in size from 7 billion to 65 billion parameters. These models were trained on publicly available datasets using millions of tokens. As a result, the LLaMA-13B model surpasses the performance of GPT-3 (175B), while the LLaMA-65B model performs similarly to top-performing models like Chinchilla-70B and PaLM-540B.
2. Alpaca
According to Stanford Alpaca, their model can rival ChatGPT, and it can be replicated for under $600 by anyone. The Alpaca 7B model is fine-tuned from the LLaMA 7B model based on 52,000 demonstrations of following instructions.
3. Vicuna
Vicuna is a model that is fine-tuned from the LLaMA model using conversations shared by users via ShareGPT. The Vicuna-13B model has achieved a quality level of over 90%* compared to OpenAI's ChatGPT and Google's Bard. It has also surpassed the LLaMA and Stanford Alpaca models in 90% of cases. Training the Vicuna model cost around $300.
4. OpenChatKit
OpenChatKit is an open-source alternative to ChatGPT that offers a comprehensive toolkit for building your own chatbot. It includes instructions for training an instruction-tuned large language model, fine-tuning the model, an extensible retrieval system for updating bot responses, and bot moderation to filter out inappropriate content.
It is evident that the GPT-NeoXT-Chat-Base-20B model has exceeded the performance of the GPT-NeoX base model in tasks such as question and answer extraction and classification.
5. GPT4ALL
GPT4ALL is a project driven by the community, and its training is based on a massive curated corpus of assistant interactions that includes code, stories, depictions, and multi-turn dialogue. The team has shared datasets, model weights, data curation processes, and training code to encourage open-source development. Additionally, they have released 4-bit quantized versions of the model that can be run on laptops. A Python client is also available for running model inference.
6.Raven RWKV
Raven RWKV 7B is an open-source chatbot that utilizes the RWKV language model to generate outputs comparable to those of ChatGPT. The model employs RNNs that can match transformers in quality and scaling while being faster and requiring less VRAM. Raven was fine-tuned on multiple datasets, including Stanford Alpaca and code-alpaca.
7. OPT
Although not as powerful as ChatGPT, OPT (Open Pre-trained Transformer Language Models) has demonstrated exceptional capabilities in zero- and few-shot learning and stereotypical bias analysis. Moreover, it can be integrated with Alpa, Colossal-AI, CTranslate2, and FasterTransformer to enhance its performance further. Despite this, OPT has gained popularity, with 624,710 monthly downloads in the text generation category.
8. Flan-T5-XXL
Flan-T5-XXL is a set of T5 models fine-tuned on various datasets phrased as instructions, resulting in a significant improvement in performance across different model classes, including PaLM, T5, and U-PaLM. The model is further fine-tuned on over 1000 additional tasks covering multiple languages, enhancing its capabilities even further.