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Qwen2.5-Math-7B fine-tuned by DeepSeek on samples from DeepSeek-R1. A 7B model that shows its working on math problems; DeepSeek reports 92.8 on MATH-500, above OpenAI o1-mini.

DeepSeek's smallest R1 distill: Qwen2.5-Math-1.5B fine-tuned on samples generated by DeepSeek-R1. It works through a math problem step by step before it gives the answer.

Matches uploaded CVs to a comma-separated list of required skills and returns a SkillMatchScore per candidate, with the parsed profile as an option.

Scores each uploaded CV against a job description you paste in. Each score comes with a written review of qualifications, experience, skills and job fit, and the detailed mode adds the parsed profile.

Reads a batch of CVs and returns one structured record per candidate (contact details, skills, certifications, job history and education) in a combined JSON or CSV file, plus one file per CV.

A 9B-parameter model from the EU-funded EuroLLM project, instruction-tuned for chat and translation in all 24 official EU languages plus 11 others, among them Turkish and Arabic.

The first instruction-tuned model in the EU-funded EuroLLM series: 1.7B parameters, tuned on EuroBlocks for translation and general instructions in 35 languages, including all 24 official EU languages.

An 8B clinical chat model from M42, fine-tuned from Llama 3 8B Instruct to answer the medical questions that general-purpose assistants are often tuned to avoid.

A 3.21B-parameter Llama 3.2 model that Meta distilled from Llama 3.1 8B and 70B, for multilingual chat and summarization. On Wiro it takes up to 128 function tools and can reply with a JSON object.

Code Llama 34B Instruct is Meta's 34-billion-parameter code model tuned to follow chat instructions. Meta groups the 34B with the 70B as the sizes that return its best results.

The 7B instruction-tuned Code Llama from Meta writes and explains code from plain-English requests. It is the smallest Instruct size in the family.

The 8-billion-parameter instruction model of InternLM3, from Shanghai AI Laboratory. It replies directly in normal mode or works through a long chain of thought in deep thinking mode.

Cohere Labs' open-weight 8B chat model, tuned for 23 languages including Arabic, Hindi, Persian and Vietnamese. The weights are under a non-commercial CC-BY-NC license.

A 12B model that Mistral AI trained jointly with NVIDIA, instruction-tuned for chat in many languages and for function calling. The card calls it a drop-in replacement for Mistral 7B.

A 1.7B-parameter English chat model from Hugging Face's SmolLM2 family, built small enough to run on-device and tuned with SFT and DPO for instruction following and function calling.

A math and science model built on Mistral 7B for problems that take several steps of reasoning. Mistral AI reports 56.6% on the MATH benchmark for it.

A 7B math model from DeepSeek, instruction-tuned on 776K English and Chinese examples. It writes step-by-step solutions and, with the recommended prompt, puts the final answer in \boxed{}.

A 3.8B-parameter Phi-3.5 chat model from Microsoft, post-trained for multi-turn and multilingual conversation across 23 listed languages. Released under the MIT license.

The 3.09B-parameter Qwen2.5 chat model that Qwen added for mobile applications. Its license, the Qwen Research License, allows non-commercial research and evaluation only.

The smallest instruction-tuned Qwen2.5 model, with 0.49B parameters and about 32,768 input tokens on Wiro. It suits routing and tagging jobs where the reply is short and the format is fixed.