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Update README.md
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README.md
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@@ -32,7 +32,7 @@ Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (
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- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
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- Number of Parameters: 32.5B
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- Number of Paramaters (Non-Embedding): 31.0B
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- Number of Layers:
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- Number of Attention Heads (GQA): 40 for Q and 8 for KV
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- Context Length: Full 131,072 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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@@ -78,7 +78,7 @@ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
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- Number of Parameters: 32.5B
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- Number of Paramaters (Non-Embedding): 31.0B
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- Number of Layers: 512
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- Number of Attention Heads (GQA): 40 for Q and 8 for KV
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- Context Length: Full 131,072 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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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=2048
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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