Model Library

DeepSeek

DeepSeek R1 05-28

DeepSeek R1 0528, a May 2025 upgrade, significantly boosts reasoning and inference in math, programming, and logic, with reduced hallucination and improved function calling.

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Llama

Llama Guard 4 12B

Designed to detect inappropriate content in both images and text used as input or generated as output by models. Llama Guard 4 can evaluate text-only and image+text inputs.

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DeepSeek

Deepseek V3 03-24

An iteration of the DeepSeek V3 model with notable improvements in reasoning capabilities across various benchmarks, including MMLU-Pro, GPQA, and AIME.

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Qwen

Qwen2-VL-7B-Instruct

An advanced vision-language model. It is purpose-built for tasks that involve both visual and textual understanding, including image captioning, visual question answering, and content generation.

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Qwen

Qwen2-VL-2B-Instruct

A powerful multimodal model that excels in visual understanding tasks, including image and video comprehension.

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Llama

Llama 3.2 1B Instruct

A lightweight, instruction-tuned large language model designed for multilingual dialogue and tasks like agentic retrieval and summarization.

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Llama

Llama 3.1 8B

An 8-billion-parameter language model, optimized for instruction following and reasoning. It delivers strong performance on benchmarks while maintaining a small, efficient footprint.

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Qwen

QwQ-32B

A 32-billion-parameter reasoning model developed through advanced reinforcement learning, noted for matching the performance of much larger state-of-the-art models on complex reasoning and coding tasks.

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Qwen

Qwen2.5-VL-7B-Instruct

A 7B-parameter multimodal model, designed for complex vision-language tasks. Ideal for document understanding, video summarization, and interactive applications requiring both visual perception and language reasoning.

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Phi

Phi-4-mini-instruct

Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data.

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Llama

Llama 4 Scout 17B (16E) Instruct

A mixture-of-experts (MoE) language model activating 17 billion parameters out of a total of 109B. Designed for assistant-style interaction and visual reasoning.

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Llama

Llama 4 Maverick 17B (128E) Instruct

High-capacity multimodal language model built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters. Supports multilingual text and image input.

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