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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 and Mistral 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.

Meta Llama

The most capable openly available LLM to date

llama3.1:8B, 70B

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) and 70B 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.

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:22B

Mistral Small is a lightweight model designed for cost-effective use in tasks like translation and summarization.

Mistral Small v24.09 is an advanced small language model of 22B parameters with improved human alignment, reasoning capabilities, and code generation.

Key features

  • Cost-efficient: Offers a mid-point between Mistral NeMo 12B and Mistral Large 2 for various use cases.
  • Versatile: Excels in tasks such as translation, summarization, and sentiment analysis.
  • Flexible deployment: Can be deployed across various platforms and environments.
  • Performance upgrade: Significant improvements over the previous Mistral Small v24.02 model.
  • Balanced solution: Provides a fast and reliable option without the need for full-blown general purpose models.
  • 128k sequence length

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/

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.