forked from ailab/Qwen1.5-MoE-A2.7B-Chat
78 lines
2.7 KiB
Markdown
78 lines
2.7 KiB
Markdown
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---
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license: other
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license_name: tongyi-qianwen
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license_link: >-
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https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat/blob/main/LICENSE
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- chat
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---
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# Qwen1.5-MoE-A2.7B-Chat
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## Introduction
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Qwen1.5-MoE is a transformer-based MoE decoder-only language model pretrained on a large amount of data.
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For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen-moe/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
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## Model Details
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Qwen1.5-MoE employs Mixture of Experts (MoE) architecture, where the models are upcycled from dense language models. For instance, `Qwen1.5-MoE-A2.7B` is upcycled from `Qwen-1.8B`. It has 14.3B parameters in total and 2.7B activated parameters during runtime, while achieching comparable performance to `Qwen1.5-7B`, it only requires 25% of the training resources. We also observed that the inference speed is 1.74 times that of `Qwen1.5-7B`.
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## Training details
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We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.
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## Requirements
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The code of Qwen1.5-MoE has been in the latest Hugging face transformers and we advise you to build from source with command `pip install git+https://github.com/huggingface/transformers`, or you might encounter the following error:
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```
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KeyError: 'qwen2_moe'.
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```
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## Quickstart
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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"Qwen/Qwen1.5-MoE-A2.7B-Chat",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B-Chat")
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prompt = "Give me a short introduction to large language model."
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=512
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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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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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For quantized models, we advise you to use the GPTQ correspondents, namely `Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4`.
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## Tips
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* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in `generation_config.json`.
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*
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