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Use Cases |
Areas: | Research, Commercial applications |
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Applications: | Text generation, Multilingual translation, Coding assistance, Mathematical computations |
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Limitations: | Not recommended for conversational models without post-training such as SFT, RLHF, etc. |
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Considerations: | Post-training is recommended for specialized conversational use cases. |
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Additional Notes | Qwen2.5 features include long-context support up to 128K tokens and can generate up to 8K tokens. It has significantly improved capabilities in instruction following, coding, and mathematics. |
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Supported Languages | languages_supported (:[), Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. ({{"languages_supported":""{"), description":"Supported languages ":null , null ] (), proficiency_level (N/A) |
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Training Details |
Data Sources: | Multiple expert data sources in coding, mathematics, and multilingual domains |
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Methodology: | Pre-training with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings |
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Context Length: | |
Model Architecture: | transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings |
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