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README.md
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@@ -23,8 +23,6 @@ Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (
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- 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.
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- 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.
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- **Long-context Support** up to 128K tokens.
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**This repo contains the 3B Qwen2.5-Coder model**, which has the following features:
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- Type: Causal Language Models
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- Number of Paramaters (Non-Embedding): 2.77B
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- Number of Layers: 36
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- Number of Attention Heads (GQA): 16 for Q and 2 for KV
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- Context Length: Full
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- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
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**We do not recommend using base language models for conversations.** Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., or fill in the middle tasks on this model.
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- 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.
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- 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.
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**This repo contains the 3B Qwen2.5-Coder model**, which has the following features:
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- Type: Causal Language Models
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- Number of Paramaters (Non-Embedding): 2.77B
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- Number of Layers: 36
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- Number of Attention Heads (GQA): 16 for Q and 2 for KV
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- Context Length: Full 32,768 tokens
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- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
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**We do not recommend using base language models for conversations.** Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., or fill in the middle tasks on this model.
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