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README.md
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@@ -24,7 +24,6 @@ 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.
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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 AWQ-quantized 4-bit instruction-tuned 1.5B 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): 1.31B
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- Number of Layers: 28
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- Number of Attention Heads (GQA): 12 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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- Quantization: AWQ 4-bit
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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.
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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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|
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**This repo contains the AWQ-quantized 4-bit instruction-tuned 1.5B 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): 1.31B
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- Number of Layers: 28
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- Number of Attention Heads (GQA): 12 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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- Quantization: AWQ 4-bit
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