This model was released on 2024-07-15 and added to Hugging Face Transformers on 2024-01-17.

</div>

</div>

Qwen2

Qwen2 is a family of large language models (pretrained, instruction-tuned and mixture-of-experts) available in sizes from 0.5B to 72B parameters. The models are built on the Transformer architecture featuring enhancements like group query attention (GQA), rotary positional embeddings (RoPE), a mix of sliding window and full attention, and dual chunk attention with YARN for training stability. Qwen2 models support multiple languages and context lengths up to 131,072 tokens.

You can find all the official Qwen2 checkpoints under the Qwen2 collection.

[!TIP] Click on the Qwen2 models in the right sidebar for more examples of how to apply Qwen2 to different language tasks.

The example below demonstrates how to generate text with [Pipeline], [AutoModel], and from the command line using the instruction-tuned models.

python

from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="Qwen/Qwen2-1.5B-Instruct",
    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'])

</hfoption> <hfoption id="AutoModel">

python

from transformers import AutoModelForCausalLM, AutoTokenizer


model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2-1.5B-Instruct",
    device_map="auto",
    attn_implementation="sdpa"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")

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)

</hfoption> <hfoption id="transformers CLI">

bash

# pip install -U flash-attn --no-build-isolation
transformers chat Qwen/Qwen2-7B-Instruct --dtype auto --attn_implementation flash_attention_2 --device 0

</hfoption> </hfoptions>

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 4-bits.

python

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


quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B")
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2-7B",
    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))

Notes

Ensure your Transformers library version is up-to-date. Qwen2 requires Transformers>=4.37.0 for full support.

Qwen2Config

[[autodoc]] Qwen2Config

Qwen2Tokenizer

[[autodoc]] Qwen2Tokenizer - save_vocabulary

Qwen2TokenizerFast

[[autodoc]] Qwen2TokenizerFast

Qwen2RMSNorm

[[autodoc]] Qwen2RMSNorm - forward

Qwen2Model

[[autodoc]] Qwen2Model - forward

Qwen2ForCausalLM

[[autodoc]] Qwen2ForCausalLM - forward

Qwen2ForSequenceClassification

[[autodoc]] Qwen2ForSequenceClassification - forward

Qwen2ForTokenClassification

[[autodoc]] Qwen2ForTokenClassification - forward

Qwen2ForQuestionAnswering

[[autodoc]] Qwen2ForQuestionAnswering - forward