Qwen_Qwen2.5-Coder-32B-Instruct.json

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    "metadata": {
        "timestamp": "2025-06-05T09:40:54.111210+00:00",
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            "name": "Qwen/Qwen2.5-Coder-32B-Instruct",
            "externalReferences": [
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                    "url": "https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct",
                    "type": "documentation"
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            "modelCard": {
                "modelParameters": {
                    "task": "text-generation",
                    "architectureFamily": "qwen2",
                    "modelArchitecture": "Qwen2ForCausalLM"
                },
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            "authors": [
                {
                    "name": "Qwen"
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                    "license": {
                        "id": "Apache-2.0",
                        "url": "https://spdx.org/licenses/Apache-2.0.html"
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            "description": "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 open-source codeLLM, with its coding abilities matching those of GPT-4o.- A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.- **Long-context Support** up to 128K tokens.**This repo contains the instruction-tuned 32B Qwen2.5-Coder model**, which has the following features:- Type: Causal Language Models- Training Stage: Pretraining & Post-training- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias- Number of Parameters: 32.5B- Number of Paramaters (Non-Embedding): 31.0B- Number of Layers: 64- Number of Attention Heads (GQA): 40 for Q and 8 for KV- Context Length: Full 131,072 tokens- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186).",
            "tags": [
                "transformers",
                "safetensors",
                "qwen2",
                "text-generation",
                "code",
                "codeqwen",
                "chat",
                "qwen",
                "qwen-coder",
                "conversational",
                "en",
                "arxiv:2409.12186",
                "arxiv:2309.00071",
                "arxiv:2407.10671",
                "base_model:Qwen/Qwen2.5-Coder-32B",
                "base_model:finetune:Qwen/Qwen2.5-Coder-32B",
                "license:apache-2.0",
                "autotrain_compatible",
                "text-generation-inference",
                "endpoints_compatible",
                "region:us"
            ]
        }
    }
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