Maintainer: AI4Chem

Total Score


Last updated 5/17/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

The ChemLLM-7B-Chat is a large language model developed by AI4Chem, a leading researcher in chemistry and molecular science. It is built on top of the InternLM-2 model, which was described in a paper published on the Hugging Face website. This model is the first open-source large language model specifically designed for tasks in chemistry and molecular science.

The ChemLLM-7B-Chat model is similar to other chemistry-focused language models like the deepseek-llm-7b-chat and internlm-chat-7b models, which also aim to provide advanced natural language processing capabilities for chemistry-related applications. However, the ChemLLM-7B-Chat model is unique in its focus on open-source availability and its integration with the broader InternLM research ecosystem.

Model inputs and outputs


  • Text: The ChemLLM-7B-Chat model accepts natural language text as input, which can include prompts, questions, or statements related to chemistry and molecular science.


  • Text: The model generates natural language responses based on the input text. These responses can include explanations, answers to questions, or generated text related to chemistry and molecular science topics.


The ChemLLM-7B-Chat model has been designed to excel in a variety of chemistry and molecular science-related tasks, such as:

  • Answering questions about chemical compounds, reactions, and properties
  • Generating text describing chemical processes and experiments
  • Assisting in the design and analysis of molecular structures
  • Providing explanations and insights about complex chemistry concepts

The model's capabilities are the result of its specialized training on a vast corpus of chemistry-focused data, which has allowed it to build a deep understanding of the domain.

What can I use it for?

The ChemLLM-7B-Chat model can be a valuable tool for researchers, students, and professionals working in the fields of chemistry, materials science, and related disciplines. Some potential use cases include:

  • Developing chemistry-focused chatbots or virtual assistants to provide information and support
  • Integrating the model into chemistry-related software and applications to enhance natural language processing capabilities
  • Conducting research and experiments by using the model to generate hypotheses, analyze data, and communicate findings
  • Creating educational resources and content to help students learn and explore chemistry concepts

The open-source nature of the ChemLLM-7B-Chat model also makes it accessible for researchers and developers to further build upon and improve, contributing to the advancement of AI in chemistry and molecular science.

Things to try

One interesting aspect of the ChemLLM-7B-Chat model is its ability to engage in open-ended dialogue and provide contextual responses. For example, you could try asking the model follow-up questions or prompting it to expand on a specific chemistry topic. This can help you explore the depth of the model's understanding and its capacity for nuanced and coherent communication.

Another interesting area to explore is the model's potential for creative applications, such as generating novel molecular designs or hypothesizing new chemical reactions. By leveraging the model's knowledge and language generation capabilities, you may be able to uncover innovative ideas and solutions in the field of chemistry.

Overall, the ChemLLM-7B-Chat model represents an exciting development in the intersection of AI and chemistry, and there are many intriguing possibilities for its use and further development.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models




Total Score


internlm-chat-7b is a 7 billion parameter AI language model developed by InternLM, a collaboration between the Shanghai Artificial Intelligence Laboratory, SenseTime Technology, the Chinese University of Hong Kong, and Fudan University. The model was trained on a vast dataset of over 2 trillion high-quality tokens, establishing a powerful knowledge base. To enable longer input sequences and stronger reasoning capabilities, it supports an 8k context window length. Compared to other models in the 7B parameter range, InternLM-7B and InternLM-Chat-7B demonstrate significantly stronger performance across a range of benchmarks, including disciplinary competence, language competence, knowledge competence, inference competence, and comprehensive understanding. Model inputs and outputs internlm-chat-7b is a text-to-text language model that can be used for a variety of natural language processing tasks. The model takes plain text as input and generates text as output. Some key highlights include: Inputs Natural language prompts**: The model can accept a wide range of natural language prompts, from simple queries to multi-sentence instructions. Context length**: The model supports an 8k context window, allowing it to reason over longer input sequences. Outputs Natural language responses**: The model generates human-readable text responses, which can range from short phrases to multi-paragraph passages. Versatile toolset**: The model provides a flexible toolset, enabling users to build their own custom workflows and applications. Capabilities internlm-chat-7b demonstrates strong performance across a range of benchmarks, including disciplinary competence, language competence, knowledge competence, inference competence, and comprehensive understanding. For example, on the MMLU benchmark, the model achieves a score of 50.8, outperforming the LLaMA-7B, Baichuan-7B, and Alpaca-7B models. Similarly, on the AGI-Eval benchmark, the model scores 42.5, again surpassing the comparison models. What can I use it for? With its robust knowledge base, strong reasoning capabilities, and versatile toolset, internlm-chat-7b can be applied to a wide range of natural language processing tasks and applications. Some potential use cases include: Content creation**: Generate high-quality written content, such as articles, reports, and stories. Question answering**: Provide informative and well-reasoned responses to a variety of questions. Task assistance**: Help users complete tasks by understanding natural language instructions and generating relevant outputs. Conversational AI**: Engage in natural, contextual dialogues and provide helpful responses to users. Things to try One interesting aspect of internlm-chat-7b is its ability to handle longer input sequences. Try providing the model with more detailed, multi-sentence prompts and observe how it is able to leverage the extended context to generate more coherent and informative responses. Additionally, experiment with the model's versatile toolset to see how you can customize and extend its capabilities to suit your specific needs.

Read more

Updated Invalid Date




Total Score


Llama3-OpenBioLLM-8B is an advanced open-source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. It builds upon the powerful foundations of the Meta-Llama-3-8B model, incorporating the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Compared to Llama3-OpenBioLLM-70B, the 8B version has a smaller parameter count but still outperforms other open-source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 on biomedical benchmarks. Model inputs and outputs Inputs Text data from the biomedical domain, such as research papers, clinical notes, and medical literature. Outputs Generated text responses to biomedical queries, questions, and prompts. Summarization of complex medical information. Extraction of biomedical entities, such as diseases, symptoms, and treatments. Classification of medical documents and data. Capabilities Llama3-OpenBioLLM-8B can efficiently analyze and summarize clinical notes, extract key medical information, answer a wide range of biomedical questions, and perform advanced clinical entity recognition. The model's strong performance on domain-specific tasks, such as Medical Genetics and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. What can I use it for? Llama3-OpenBioLLM-8B can be a valuable tool for researchers, clinicians, and developers working in the healthcare and life sciences fields. It can be used to accelerate medical research, improve clinical decision-making, and enhance access to biomedical knowledge. Some potential use cases include: Summarizing complex medical records and literature Answering medical queries and providing information to patients or healthcare professionals Extracting relevant biomedical entities from text Classifying medical documents and data Generating medical reports and content Things to try One interesting aspect of Llama3-OpenBioLLM-8B is its ability to leverage its deep understanding of medical terminology and context to accurately annotate and categorize clinical entities. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. You could try experimenting with the model's entity recognition abilities on your own biomedical text data to see how it performs. Another interesting feature is the model's strong performance on biomedical question-answering tasks, such as PubMedQA. You could try prompting the model with a range of medical questions and see how it responds, paying attention to the level of detail and accuracy in the answers.

Read more

Updated Invalid Date




Total Score


LWM-Text-1M-Chat is an open-source auto-regressive language model developed by LargeWorldModel. It is based on the LLaMA-2 model and trained on a subset of the Books3 dataset. The model is designed for text generation and chat-like dialogue tasks. Compared to similar models like Llama-2-13b-chat and Llama-2-7b-chat-hf, LWM-Text-1M-Chat was trained on a smaller dataset of 800 Books3 documents with 1M tokens. This may result in more specialized capabilities compared to the larger Llama-2 models, which were trained on 2 trillion tokens of data. Model inputs and outputs Inputs The LWM-Text-1M-Chat model takes text as input for text generation and chat-like tasks. Outputs The model generates text as output, producing coherent and contextually-appropriate responses. Capabilities The LWM-Text-1M-Chat model can be used for a variety of text generation tasks, including chat-based dialogue, content creation, and language understanding. Due to its specialized training on a subset of Books3, the model may excel at tasks like story writing, poetry generation, and answering questions about literature and humanities topics. What can I use it for? Developers and researchers can use LWM-Text-1M-Chat for projects involving text-based AI assistants, creative writing tools, and language understanding applications. The model's training on a literary dataset also makes it suitable for use cases in education, academic research, and creative industries. Things to try Given the model's specialized training on a literary dataset, users could experiment with prompts related to fiction, poetry, and analysis of literary works. Additionally, the model's chat-like capabilities lend themselves well to conversational AI applications where a more personalized, engaging style of interaction is desired.

Read more

Updated Invalid Date




Total Score


deepseek-llm-7b-chat is a 7 billion parameter language model developed by DeepSeek AI. It has been trained from scratch on a vast 2 trillion token dataset, with 87% code and 13% natural language in both English and Chinese. DeepSeek AI also offers larger model sizes up to 67 billion parameters with the deepseek-llm-67b-chat model, as well as a series of code-focused models under the deepseek-coder line. The deepseek-llm-7b-chat model has been fine-tuned on extra instruction data, allowing it to engage in natural language conversations. This contrasts with the base deepseek-llm-7b-base model, which is focused more on general language understanding. The deepseek-vl-7b-chat takes the language model a step further by incorporating vision-language capabilities, enabling it to understand and reason about visual content as well. Model inputs and outputs Inputs Text**: The model accepts natural language text as input, which can include prompts, conversations, or other types of text-based communication. Images**: Some DeepSeek models, like deepseek-vl-7b-chat, can also accept image inputs to enable multimodal understanding and generation. Outputs Text Generation**: The primary output of the model is generated text, which can range from short responses to longer form content. The model is able to continue a conversation, answer questions, or generate original text. Code Generation**: For the deepseek-coder models, the output includes generated code snippets and programs in a variety of programming languages. Capabilities The deepseek-llm-7b-chat model demonstrates strong natural language understanding and generation capabilities. It can engage in open-ended conversations, answering questions, providing explanations, and even generating creative content. The model's large training dataset and fine-tuning on instructional data gives it a broad knowledge base and the ability to follow complex prompts. For users looking for more specialized capabilities, the deepseek-vl-7b-chat and deepseek-coder models offer additional functionality. The deepseek-vl-7b-chat can process and reason about visual information, making it well-suited for tasks involving diagrams, images, and other multimodal content. The deepseek-coder series focuses on code-related abilities, demonstrating state-of-the-art performance on programming tasks and benchmarks. What can I use it for? The deepseek-llm-7b-chat model can be a versatile tool for a wide range of applications. Some potential use cases include: Conversational AI**: Develop chatbots, virtual assistants, or dialogue systems that can engage in natural, contextual conversations. Content Generation**: Create original text content such as articles, stories, or scripts. Question Answering**: Build applications that can provide informative and insightful answers to user questions. Summarization**: Condense long-form text into concise, high-level summaries. For users with more specialized needs, the deepseek-vl-7b-chat and deepseek-coder models open up additional possibilities: Multimodal Reasoning**: Develop applications that can understand and reason about the relationships between text and visual information, like diagrams or technical documentation. Code Generation and Assistance**: Build tools that can generate, explain, or assist with coding tasks across a variety of programming languages. Things to try One interesting aspect of the deepseek-llm-7b-chat model is its ability to engage in open-ended, multi-turn conversations. Try providing the model with a prompt that sets up a scenario or persona, and see how it responds and builds upon the dialogue. You can also experiment with giving the model specific instructions or tasks to test its adaptability and problem-solving skills. For users interested in the multimodal capabilities of the deepseek-vl-7b-chat model, try providing the model with a mix of text and images to see how it interprets and reasons about the combined information. This could involve describing an image and having the model generate a response, or asking the model to explain the content of a technical diagram. Finally, the deepseek-coder models offer a unique opportunity to explore the intersection of language and code. Try prompting the model with a partially complete code snippet and see if it can fill in the missing pieces, or ask it to explain the functionality of a given piece of code.

Read more

Updated Invalid Date