Qwen2MoE

This model was released on 2024-07-15 and added to Hugging Face Transformers on 2024-03-27.

Qwen2MoE

Qwen2MoE is a Mixture-of-Experts (MoE) variant of Qwen2, available as a base model and an aligned chat model. It uses SwiGLU activation, group query attention and a mixture of sliding window attention and full attention. The tokenizer can also be adapted to multiple languages and codes.

The MoE architecture uses upcyled models from the dense language models. For example, Qwen1.5-MoE-A2.7B is upcycled from Qwen-1.8B. It has 14.3B parameters but only 2.7B parameters are activated during runtime.

You can find all the original checkpoints in the Qwen1.5 collection.

Tip

Click on the Qwen2MoE models in the right sidebar for more examples of how to apply Qwen2MoE to different language tasks.

The example below demonstrates how to generate text with Pipeline, AutoModel, and from the command line.

import torch
from transformers import pipeline

pipe = pipeline(
    task="text-generation",
    model="Qwen/Qwen1.5-MoE-A2.7B",
    dtype=torch.bfloat16,
    device_map=0
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Tell me about the Qwen2 model family."},
]
outputs = pipe(messages, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"][-1]['content'])

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen1.5-MoE-A2.7B-Chat",
    dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B-Chat")

prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    model_inputs.input_ids,
    cache_implementation="static",
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

```bash transformers chat Qwen/Qwen1.5-MoE-A2.7B-Chat --dtype auto --attn_implementation flash_attention_2 ```

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to quantize the weights to 8-bits.

# pip install -U flash-attn --no-build-isolation
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_8bit=True
)

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B-Chat")
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen1.5-MoE-A2.7B-Chat",
    dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=quantization_config,
    attn_implementation="flash_attention_2"
)

inputs = tokenizer("The Qwen2 model family is", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Qwen2MoeConfig

[[autodoc]] Qwen2MoeConfig

Qwen2MoeModel

[[autodoc]] Qwen2MoeModel - forward

Qwen2MoeForCausalLM

[[autodoc]] Qwen2MoeForCausalLM - forward

Qwen2MoeForSequenceClassification

[[autodoc]] Qwen2MoeForSequenceClassification - forward

Qwen2MoeForTokenClassification

[[autodoc]] Qwen2MoeForTokenClassification - forward

Qwen2MoeForQuestionAnswering

[[autodoc]] Qwen2MoeForQuestionAnswering - forward