Viper-Coder-v1.4 is based on the Qwen 2.5 14B modality architecture, designed to be the best for coding and reasoning tasks. It has been fine-tuned on a synthetic dataset leveraging the latest coding logits and CoT datasets, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex coding tasks, instruction-following, and text generation. 1. Best-in-Class Coding Proficiency: Enhanced understanding of programming languages, debugging, and code generation. 2. Fine-Tuned Instruction Following: Optimized for precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (8K+ tokens). 3. Advanced Logical & Mathematical Reasoning: Improved multi-step problem-solving and theorem proving. 4. Long-Context Mastery: Handles up to 128K tokens with an output capability of 8K tokens per response. 5. Multilingual Code Support: Excels in Python, JavaScript, C++, Java, SQL, and other major programming languages, with documentation in 29+ languages. — Elite Coding & Debugging:…
Модальности:
Генерация текста
Области применения:
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Задача: Генерация текста
Автор: prithivMLmods
Теги: qwen2, text-generation-inference, coder, trl, conversational, en, zh, endpoints_compatible
Лайков: 4 | Загрузок: 12
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
