Qwen2.5-Coder-14B-Instruct
Qwen2.5-Coder-14B-Instruct is a high-performance AI model optimized for code generation, debugging, and reasoning.
English
English
Qwen2.5-Coder-14B-Instruct is an instruction fine-tuning model optimized for code tasks developed by Qwen. It is suitable for code generation, reasoning, debugging and other application scenarios.
Model architecture
Contains 48 Transformer layers, using rotation position embedding (RoPE), SwiGLU activation function, RMSNorm normalization and attention mechanism with QKV bias.
Using Grouped Query Attention (GQA), there are 40 query headers and 8 key-value headers, designed for efficient code processing.
Parameter quantity
The total number of parameters is 14.7 billion, of which 13.1 billion are used for the non-embedded part.
context length
Supports context lengths up to 131,072 tokens and supports handling of large code bases and long documents through YaRN technology.
Performance
Significantly superior performance in code generation, inference, and code repair, as well as strong performance in mathematical calculations and general-purpose tasks.
The basic model provides a variety of parameter sizes, including 0.5B, 1.5B, 3B, 7B, 14B and 32B, suitable for code completion and basic tasks.
Instruction fine-tuning model Optimized for interactive tasks such as code generation and debugging, the 14B-Instruct model is ideal for chat-based application scenarios.
Python version : 3.9 or higher.
Transformers library : version 4.37.0 or higher, supports the integration of Qwen2 series models.
The sample code for loading a model using Hugging Face's transformers library is as follows:
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-14B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name)
This model can efficiently complete tasks such as code generation and debugging.
Check whether the network connection is stable, try using a proxy or mirror source; confirm whether you need to log in to your account or provide an API key. If the path or version is wrong, the download will fail.
Make sure you have installed the correct version of the framework, check the version of the dependent libraries required by the model, and update the relevant libraries or switch the supported framework version if necessary.
Use a local cache model to avoid repeated downloads; or switch to a lighter model and optimize the storage path and reading method.
Enable GPU or TPU acceleration, use batch data processing methods, or choose a lightweight model such as MobileNet to increase speed.
Try quantizing the model or using gradient checkpointing to reduce the memory requirements. You can also use distributed computing to spread the task across multiple devices.
Check whether the input data format is correct, whether the preprocessing method matching the model is in place, and if necessary, fine-tune the model to adapt to specific tasks.
