Qwen / Qwen2-1.5B-Instruct-AWQ

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Total runs: 10.6K
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Model's Last Updated: August 21 2024
text-generation

Introduction of Qwen2-1.5B-Instruct-AWQ

Model Details of Qwen2-1.5B-Instruct-AWQ

Qwen2-1.5B-Instruct-AWQ

Introduction

Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 1.5B Qwen2 model.

Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.

For more details, please refer to our blog , GitHub , and Documentation .

Model Details

Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.

Training details

We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.

Requirements

The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install transformers>=4.37.0 , or you might encounter the following error:

KeyError: 'qwen2'
Quickstart

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

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

prompt = "Give me a short introduction to large language model."
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(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
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]
Benchmark and Speed

To compare the generation performance between bfloat16 (bf16) and quantized models such as GPTQ-Int8, GPTQ-Int4, and AWQ, please consult our Benchmark of Quantized Models . This benchmark provides insights into how different quantization techniques affect model performance.

For those interested in understanding the inference speed and memory consumption when deploying these models with either transformer or vLLM , we have compiled an extensive Speed Benchmark .

Citation

If you find our work helpful, feel free to give us a cite.

@article{qwen2,
  title={Qwen2 Technical Report},
  year={2024}
}

Runs of Qwen Qwen2-1.5B-Instruct-AWQ on huggingface.co

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More Information About Qwen2-1.5B-Instruct-AWQ huggingface.co Model

More Qwen2-1.5B-Instruct-AWQ license Visit here:

https://choosealicense.com/licenses/apache-2.0

Qwen2-1.5B-Instruct-AWQ huggingface.co

Qwen2-1.5B-Instruct-AWQ huggingface.co is an AI model on huggingface.co that provides Qwen2-1.5B-Instruct-AWQ's model effect (), which can be used instantly with this Qwen Qwen2-1.5B-Instruct-AWQ model. huggingface.co supports a free trial of the Qwen2-1.5B-Instruct-AWQ model, and also provides paid use of the Qwen2-1.5B-Instruct-AWQ. Support call Qwen2-1.5B-Instruct-AWQ model through api, including Node.js, Python, http.

Qwen2-1.5B-Instruct-AWQ huggingface.co Url

https://huggingface.co/Qwen/Qwen2-1.5B-Instruct-AWQ

Qwen Qwen2-1.5B-Instruct-AWQ online free

Qwen2-1.5B-Instruct-AWQ huggingface.co is an online trial and call api platform, which integrates Qwen2-1.5B-Instruct-AWQ's modeling effects, including api services, and provides a free online trial of Qwen2-1.5B-Instruct-AWQ, you can try Qwen2-1.5B-Instruct-AWQ online for free by clicking the link below.

Qwen Qwen2-1.5B-Instruct-AWQ online free url in huggingface.co:

https://huggingface.co/Qwen/Qwen2-1.5B-Instruct-AWQ

Qwen2-1.5B-Instruct-AWQ install

Qwen2-1.5B-Instruct-AWQ is an open source model from GitHub that offers a free installation service, and any user can find Qwen2-1.5B-Instruct-AWQ on GitHub to install. At the same time, huggingface.co provides the effect of Qwen2-1.5B-Instruct-AWQ install, users can directly use Qwen2-1.5B-Instruct-AWQ installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

Qwen2-1.5B-Instruct-AWQ install url in huggingface.co:

https://huggingface.co/Qwen/Qwen2-1.5B-Instruct-AWQ

Url of Qwen2-1.5B-Instruct-AWQ

Qwen2-1.5B-Instruct-AWQ huggingface.co Url

Provider of Qwen2-1.5B-Instruct-AWQ huggingface.co

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