0.5B code-specific base model
According to the model card, this is the 0.5B Qwen2.5-Coder causal language model at the pretraining stage, within a series covering 0.5B to 32B parameters.
Open Source Model Profile · Qwen
Qwen2.5-Coder-0.5B is a 0.49B-parameter Qwen2 code-generation model from Qwen at the pretraining stage, with a documented 32,768-token context.
Qwen2.5-Coder-0.5B is published by Qwen as a code-specific Qwen2 text-generation model. The captured configuration identifies Qwen2ForCausalLM and Safetensors metadata reports 494,032,768 parameters. According to the model card, this repo holds the 0.5B pretraining-stage model with 32,768 tokens of context, and card data records apache-2.0.
According to the model card, this is the 0.5B Qwen2.5-Coder causal language model at the pretraining stage, within a series covering 0.5B to 32B parameters.
According to the model card, the model supports 32,768 tokens of context with grouped-query attention of 14 query heads and 2 key-value heads.
According to the model card, the series targets code generation, code reasoning, and code fixing, with a stated foundation for code-agent applications.
According to the model card, GPU-memory requirements and throughput are documented through an external results link rather than in the extracted record.
Source: Qwen/Qwen2.5-Coder-0.5B
Captured: Unknown. Processed: 2026-09-07T19:34:35.863296+00:00.
Qwen2.5-Coder-0.5B Introduction Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: Significantly improvements in code generation , code reasoning and code fixing . Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art…
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