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HomeLLMsQwen3 14B Instruct

Qwen3 14B Instruct

by OpenPipe

Open source · 136k downloads · 12 likes

1.4
(12 reviews)ChatAPI & Local
About

Qwen3 14B Instruct is an optimized version of the Qwen3 model designed for instruction-based tasks and fine-tuning, with enhanced compatibility for training frameworks like OpenPipe. It retains the advanced general capabilities of its base model while improving response consistency through explicit handling of reflection tags, ensuring uniformity between training and inference. This model excels in dialogue tasks, structured text generation, and conversational assistance, while remaining adaptable for customization. Its long context window (up to 131,072 tokens) makes it particularly well-suited for analyzing complex documents or extended interactions. What sets it apart is its balance between raw performance and flexibility, making it ideal for developers seeking to refine the model for their specific needs.

Documentation

Qwen3-14B

Chat

Qwen3-14B-Instruct Highlights

OpenPipe/Qwen3-14B-Instruct is a finetune friendly instruct variant of Qwen3-14B. Qwen3 release does not include a 14B Instruct (non-thinking) model, this fork introduces an updated chat template that makes Qwen3-14B non-thinking by default and be highly compatible with OpenPipe and other finetuning frameworks.

The default Qwen3 chat template does not render <think></think> tags on the previous assistant message, which can lead to inconsistencies between training and generation. This version resolves that issue by adding <think></think> tags to all assistant prompts and generation templates to ensure message format consistency during both training and inference.

The model retains the strong general capabilities of Qwen3-14B while providing a more finetuning friendly chat template.

Model Overview

Qwen3-14B has the following features:

  • Type: Causal Language Models
  • Training Stage: Pretraining & Post-training
  • Number of Parameters: 14.8B
  • Number of Paramaters (Non-Embedding): 13.2B
  • Number of Layers: 40
  • Number of Attention Heads (GQA): 40 for Q and 8 for KV
  • Context Length: 32,768 natively and 131,072 tokens with YaRN.

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Capabilities & Tags
transformerssafetensorsqwen3text-generationconversationaltext-generation-inferenceendpoints_compatible
Links & Resources
Specifications
CategoryChat
AccessAPI & Local
LicenseOpen Source
PricingOpen Source
Parameters14B parameters
Rating
1.4

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