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RichardErkhov/
Qwen_-_Qwen2-72B-Instruct-gguf

Name: Qwen_-_Qwen2-72B-Instruct-gguf
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Qwen2-72B-Instruct - GGUF

Model creator: https://huggingface.co/Qwen/
Original model: https://huggingface.co/Qwen/Qwen2-72B-Instruct/

Name	Quant method	Size
Qwen2-72B-Instruct.Q2_K.gguf	Q2_K	27.76GB
Qwen2-72B-Instruct.IQ3_XS.gguf	IQ3_XS	30.59GB
Qwen2-72B-Instruct.IQ3_S.gguf	IQ3_S	32.12GB
Qwen2-72B-Instruct.Q3_K_S.gguf	Q3_K_S	32.12GB
Qwen2-72B-Instruct.IQ3_M.gguf	IQ3_M	33.07GB
Qwen2-72B-Instruct.Q3_K.gguf	Q3_K	35.11GB
Qwen2-72B-Instruct.Q3_K_M.gguf	Q3_K_M	35.11GB
Qwen2-72B-Instruct.Q3_K_L.gguf	Q3_K_L	36.79GB
Qwen2-72B-Instruct.IQ4_XS.gguf	IQ4_XS	37.4GB
Qwen2-72B-Instruct.Q4_0.gguf	Q4_0	38.4GB
Qwen2-72B-Instruct.IQ4_NL.gguf	IQ4_NL	38.9GB
Qwen2-72B-Instruct.Q4_K_S.gguf	Q4_K_S	40.88GB
Qwen2-72B-Instruct.Q4_K.gguf	Q4_K	44.16GB
Qwen2-72B-Instruct.Q4_K_M.gguf	Q4_K_M	44.16GB
Qwen2-72B-Instruct.Q4_1.gguf	Q4_1	42.56GB
Qwen2-72B-Instruct.Q5_0.gguf	Q5_0	46.72GB
Qwen2-72B-Instruct.Q5_K_S.gguf	Q5_K_S	47.85GB
Qwen2-72B-Instruct.Q5_K.gguf	Q5_K	50.71GB
Qwen2-72B-Instruct.Q5_K_M.gguf	Q5_K_M	50.71GB
Qwen2-72B-Instruct.Q5_1.gguf	Q5_1	50.88GB
Qwen2-72B-Instruct.Q6_K.gguf	Q6_K	59.93GB
Qwen2-72B-Instruct.Q8_0.gguf	Q8_0	71.96GB

Original model description:

license: other license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE language:

en pipeline_tag: text-generation tags:
chat

Qwen2-72B-Instruct

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 72B 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.

Qwen2-72B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to this section for detailed instructions on how to deploy Qwen2 for handling long texts.

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.

python

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

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2-72B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-72B-Instruct")

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]

Processing Long Texts

To handle extensive inputs exceeding 32,768 tokens, we utilize YARN, a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.

For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps:

Install vLLM: You can install vLLM by running the following command.

pip install "vllm>=0.4.3"

Or you can install vLLM from source.

Configure Model Settings: After downloading the model weights, modify the config.json file by including the below snippet:

json
1 { 2 "architectures": [ 3 "Qwen2ForCausalLM" 4 ], 5 // ... 6 "vocab_size": 152064, 7 8 // adding the following snippets 9 "rope_scaling": { 10 "factor": 4.0, 11 "original_max_position_embeddings": 32768, 12 "type": "yarn" 13 } 14 }

This snippet enable YARN to support longer contexts.
Model Deployment: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command:

python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-72B-Instruct --model path/to/weights

Then you can access the Chat API by:

bash
1curl http://localhost:8000/v1/chat/completions \ 2 -H "Content-Type: application/json" \ 3 -d '{ 4 "model": "Qwen2-72B-Instruct", 5 "messages": [ 6 {"role": "system", "content": "You are a helpful assistant."}, 7 {"role": "user", "content": "Your Long Input Here."} 8 ] 9 }'

For further usage instructions of vLLM, please refer to our Github.

Note: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the rope_scaling configuration only when processing long contexts is required.

Evaluation

We briefly compare Qwen2-72B-Instruct with similar-sized instruction-tuned LLMs, including our previous Qwen1.5-72B-Chat. The results are shown as follows:

Datasets	Llama-3-70B-Instruct	Qwen1.5-72B-Chat	Qwen2-72B-Instruct
English
MMLU	82.0	75.6	82.3
MMLU-Pro	56.2	51.7	64.4
GPQA	41.9	39.4	42.4
TheroemQA	42.5	28.8	44.4
MT-Bench	8.95	8.61	9.12
Arena-Hard	41.1	36.1	48.1
IFEval (Prompt Strict-Acc.)	77.3	55.8	77.6
Coding
HumanEval	81.7	71.3	86.0
MBPP	82.3	71.9	80.2
MultiPL-E	63.4	48.1	69.2
EvalPlus	75.2	66.9	79.0
LiveCodeBench	29.3	17.9	35.7
Mathematics
GSM8K	93.0	82.7	91.1
MATH	50.4	42.5	59.7
Chinese
C-Eval	61.6	76.1	83.8
AlignBench	7.42	7.28	8.27

Citation

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

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

RichardErkhov/Qwen_-_Qwen2-72B-Instruct-gguf