SAIFIINDUSTRIES/Qwen2.5-14B-Instruct-1M

TEXT GENERATIONPricing:Input $0.431 / Cached $0.0862 / Output $1.12Concurrent Unit Cost:1Model Size:14.8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 5, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Qwen2.5-14B-Instruct-1M is a 14.7 billion parameter causal language model developed by Qwen, part of the Qwen2.5 series. This model is specifically optimized for ultra-long context tasks, supporting an impressive context length of up to 1 million tokens while maintaining strong performance on shorter tasks. It utilizes a transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. Its primary strength lies in efficiently processing and generating content for extremely long sequences, making it suitable for applications requiring extensive document understanding or generation.

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Qwen2.5-14B-Instruct-1M: Ultra-Long Context LLM

Qwen2.5-14B-Instruct-1M is a 14.7 billion parameter instruction-tuned causal language model from the Qwen2.5 series, developed by Qwen. Its standout feature is its unprecedented context length of up to 1 million tokens, making it a leader in processing and understanding extremely long sequences. While excelling in long-context scenarios, it also maintains robust performance on standard short-context tasks.

Key Capabilities and Features

  • 1 Million Token Context Window: Designed to handle massive inputs, significantly outperforming previous versions in long-context tasks.
  • Optimized Inference Framework: Leverages a custom vLLM implementation with sparse attention and length extrapolation for enhanced accuracy and efficiency with ultra-long texts. This framework can achieve 3 to 7 times speedup for 1M token sequences.
  • Transformer Architecture: Built on a robust transformer architecture incorporating RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
  • High VRAM Requirements: For 1M token processing, the 14B model requires at least 320GB VRAM, indicating its advanced computational demands.
  • Efficient Deployment: Provides detailed guidance for deployment using vLLM, including parameter tuning for tensor_parallel_size, max_model_len, and max_num_batched_tokens.

Ideal Use Cases

  • Advanced Document Analysis: Perfect for tasks requiring comprehension across very large documents, such as legal briefs, research papers, or extensive codebases.
  • Long-form Content Generation: Suitable for generating lengthy articles, reports, or creative narratives that require maintaining coherence over extended contexts.
  • Complex Information Retrieval: Excels in scenarios where relevant information might be scattered across vast amounts of text.
  • Applications Requiring Deep Contextual Understanding: Any application where the ability to process and reason over extremely long inputs is critical.

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