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Models

Available models

GenAI4Science portal is currently optimized for enabling multiple models, so that users can try as much as possible. Various larger model sizes of the Llama, Gemma, Mistral and DeepSeek model families are available, including versions optimized for coding.

Since only one large model (70B, 90B, 123B) can fit in the memory of the GPU card at the same time, it happens that you have to wait a bit when changing models.

Each backend instance can handle limited number of requests per model at the same time (usually 4). If this limit has been reached, then the request is waiting in the backend queue. When this queue is full, the following error message is displayed:

Ollama: 503, message='Service Unavailable'

After a short wait, you should press the "Regenerate" button below the message to try again.

View the active users indicator and click on it to see the currently used models in the users menu.

DeepSeek

deepseek-r1:32b, 70b

DeepSeek’s first-generation reasoning models, achieving performance comparable to OpenAI-o1 across math, code, and reasoning tasks.

Distilled models

DeepSeek team has demonstrated that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models.

GenAI4Science supports DeepSeek-R1-Distill-Llama-70B and DeepSeek-R1-Distill-Qwen-32B.

License

The model weights are licensed under the MIT License. DeepSeek-R1 series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that:

The Qwen distilled models are derived from Qwen-2.5 series, which are originally licensed under Apache 2.0 License, and now finetuned with 800k samples curated with DeepSeek-R1.

The Llama 8B distilled model is derived from Llama3.1-8B-Base and is originally licensed under llama3.1 license.

The Llama 70B distilled model is derived from Llama3.3-70B-Instruct and is originally licensed under llama3.3 license.

Google Gemma

gemma2:27B

Google’s Gemma 2 model is available in three sizes, 2B, 9B and 27B, featuring a brand new architecture designed for class leading performance and efficiency. GenAI4Science supports the 27B model size.

Class leading performance

At 27 billion parameters, Gemma 2 delivers performance surpassing models more than twice its size in benchmarks. This breakthrough efficiency sets a new standard in the open model landscape.

Intended Usage

Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.

Content Creation and Communication

  • Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts.
  • Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications.
  • Text Summarization: Generate concise summaries of a text corpus, research papers, or reports.

Research and Education

  • Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field.
  • Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice.
  • Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics.

Meta Llama

The most capable openly available LLM to date

llama3.1:8B

Llama 3.1 is a new state-of-the-art model from Meta available in 8B, 70B and 405B parameter sizes.

GenAI4Science supports 8B (default model) from Llama 3.1 family.

The upgraded versions of the 8B and 70B models are multilingual and have a significantly longer context length of 128K, state-of-the-art tool use, and overall stronger reasoning capabilities. This enables Meta’s latest models to support advanced use cases, such as long-form text summarization, multilingual conversational agents, and coding assistants.

Meta also has made changes to their license, allowing developers to use the outputs from Llama models to improve other models.

Model evaluations

For this release, Meta has evaluation the performance on over 150 benchmark datasets that span a wide range of languages. In addition, Meta performed extensive human evaluations that compare Llama 3.1 with competing models in real-world scenarios. Meta’s experimental evaluation suggests that our flagship model 405B is competitive with leading foundation models across a range of tasks, including GPT-4, GPT-4o, and Claude 3.5 Sonnet. Additionally, Meta’s smaller models are competitive with closed and open models that have a similar number of parameters.

llama3.2:3B

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.

The 3B model outperforms the Gemma 2 2.6B and Phi 3.5-mini models on tasks such as:

  • Following instructions
  • Summarization
  • Prompt rewriting
  • Tool use

Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages.

llama3.2-vision:90B

The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a collection of instruction-tuned image reasoning generative models in 11B and 90B sizes (text + images in / text out). The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The models outperform many of the available open source and closed multimodal models on common industry benchmarks.

Supported Languages: For text only tasks, English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Note for image+text applications, English is the only language supported.

llama3.3:70B

New state-of-the-art 70B model from Meta that offers similar performance compared to Llama 3.1 405B model.

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases.

Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

New capabilities

This release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.

Tool-use: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.

Multilinguality: Llama 3.3 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.

Intended Use

Intended Use Cases Llama 3.3 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.3 model also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.3 Community License allows for these use cases.

Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.3 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.

Note: Llama 3.3 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.3 models for languages beyond the 8 supported languages provided they comply with the Llama 3.3 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.3 in additional languages is done in a safe and responsible manner.

codellama:13B

Code Llama is a model for generating and discussing code, built on top of Llama 2. It’s designed to make workflows faster and efficient for developers and make it easier for people to learn how to code. It can generate both code and natural language about code. Code Llama supports many of the most popular programming languages used today, including Python, C++, Java, PHP, Typescript (Javascript), C#, Bash and more.

GenAI4Science supports the 13B model size.

More information: How to prompt Code Llama

Mistral

Mistral AI is a French company specializing in artificial intelligence products.

mistral-large:123B

Mistral Large 2 is Mistral's new flagship model that is significantly more capable in code generation, mathematics, and reasoning with 128k context window and support for dozens of languages.

Mistral-Large-Instruct-2407 is an advanced dense Large Language Model (LLM) of 123B parameters with state-of-the-art reasoning, knowledge and coding capabilities. Key features

  • Multi-lingual by design: Dozens of languages supported, including English, French, German, Spanish, Italian, Chinese Japanese, Korean, Portuguese, Dutch and Polish.
  • Proficient in coding: Trained on 80+ coding languages such as Python, Java, C, C++, JavacScript, and Bash. Also trained on more specific languages such as Swift and Fortran.
  • Agentic-centric: Best-in-class agentic capabilities with native function calling and JSON outputting.
  • Advanced Reasoning: State-of-the-art mathematical and reasoning capabilities.
  • Mistral Research License: Allows usage and modification for research and non-commercial usages.
  • Large Context: A large 128k context window.

mistral-small:24B

Mistral Small 3 sets a new benchmark in the “small” Large Language Models category below 70B.

Key features

  • Multilingual: Supports dozens of languages, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.
  • Agent-Centric: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
  • Advanced Reasoning: State-of-the-art conversational and reasoning capabilities.
  • Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
  • Context Window: A 32k context window.
  • System Prompt: Maintains strong adherence and support for system prompts.
  • Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.

codestral:22B

Codestral is Mistral AI’s first-ever code model designed for code generation tasks. It is a 22B model. Fluent in 80+ programming languages

Codestral is trained on a dataset of over 80 programming languages, including Python, Java, C, C++, JavaScript, Swift, Fortran and Bash.

The model can complete coding functions, write tests, and complete any partial code using a fill-in-the-middle mechanism.

More information: https://mistral.ai/news/codestral/