New blocks

WizardLM-2 8x22B is Microsoft's latest Mixture-of-Experts model, using 8 expert networks of 22B parameters each. This 176B total parameter architecture delivers competitive performance in knowledge-intensive tasks.

Llama-3-8B-Instruct is Meta's compact dialogue model with 8B parameters, trained on 15T tokens. This instruction-tuned decoder demonstrates competitive performance against larger closed-source models in human evaluations.

Qwen-2.5-72B-Instruct leads the Qwen2.5 series at 72B parameters. This instruction-tuned transformer excels in language understanding, coding, mathematics, reasoning, and multilingual tasks.

A high-performing open embedding model with a large token context window

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DeepSeek R1 Distill Llama 8B distills DeepSeek R1's reasoning into Llama-3.1's 8B architecture. This compact model combines Llama's efficiency with DeepSeek's advanced capabilities through knowledge distillation.

MythoMax L2 13B combines MythoLogic-L2's comprehension with Huginn's writing capabilities through innovative tensor merging. This 13B hybrid model balances robust understanding with creative expression.

L3-8B-Stheno-v3.2 is an 8B parameter model optimized for dynamic role-play and character immersion. This specialized model focuses on consistent character portrayal and contextual responses.

DeepSeek R1 Distill Qwen 32B distills Qwen 2.5 and DeepSeek R1 expertise into a powerful 32B model. Outperforms OpenAI's o1-mini with impressive math capabilities (AIME: 72.6%, MATH-500: 94.3%) and coding skills (CodeForces: 1691).

DeepSeek R1 Distill Qwen 14B leverages Qwen 2.5 and DeepSeek R1 for superior performance. This 14B distilled model outperforms OpenAI's o1-mini, scoring 74% on MMLU and excelling in math tasks (AIME: 69.7%, MATH-500: 93.9%)

The latest series of Code-Specific Qwen models, with significant improvements in code generation, code reasoning, and code fixing

Mistral-Nemo combines NVIDIA collaboration with impressive 128k context in a 12B package. This Apache-licensed model supports 11 languages and function calling, while outperforming similarly-sized competitors.

Meta's Llama 3.1 70B-instruct model excels in multilingual dialogue and complex reasoning. This instruction-tuned powerhouse sits between the efficient 8B and massive 405B variants, offering optimal performance-to-size ratio and outperforming many closed-source competitors.

The latest series of Code-Specific Qwen models, with significant improvements in code generation, code reasoning, and code fixing

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