Ziya-Coding-34B-v1.0-AWQ

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Ziya Coding 34B v1.0 - AWQ

• Model creator: Fengshenbang-LM
• Original model: Ziya Coding 34B v1.0

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Description

This repo contains AWQ model files for Fengshenbang-LM's Ziya Coding 34B v1.0.

About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.

It is also now supported by continuous batching server vLLM, allowing use of Llama AWQ models for high-throughput concurrent inference in multi-user server scenarios.

As of September 25th 2023, preliminary Llama-only AWQ support has also been added to Huggingface Text Generation Inference (TGI).

Note that, at the time of writing, overall throughput is still lower than running vLLM or TGI with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.

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Repositories available

• AWQ model(s) for GPU inference.
• GPTQ models for GPU inference, with multiple quantisation parameter options.
• 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
• Fengshenbang-LM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

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Prompt template: Ziya

<human>: 
Please Complete the given function below according to the docstring: 
{prompt}
<bot>: 

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Licensing

The creator of the source model has listed its license as gpl-3.0, and this quantization has therefore used that same license.

As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: Fengshenbang-LM's Ziya Coding 34B v1.0.

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Provided files, and AWQ parameters

For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.

Models are released as sharded safetensors files.

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Serving this model from vLLM

Documentation on installing and using vLLM can be found here.

Note: at the time of writing, vLLM has not yet done a new release with AWQ support.

If you try the vLLM examples below and get an error about quantization being unrecognised, or other AWQ-related issues, please install vLLM from Github source.

• When using vLLM as a server, pass the --quantization awq parameter, for example:

python3 python -m vllm.entrypoints.api_server --model TheBloke/Ziya-Coding-34B-v1.0-AWQ --quantization awq --dtype half

When using vLLM from Python code, pass the quantization=awq parameter, for example:

from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="TheBloke/Ziya-Coding-34B-v1.0-AWQ", quantization="awq", dtype="half")

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

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Serving this model from Text Generation Inference (TGI)

Use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text-generation-inference:1.1.0

Example Docker parameters:

--model-id TheBloke/Ziya-Coding-34B-v1.0-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096

Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):

pip3 install huggingface-hub

from huggingface_hub import InferenceClient

endpoint_url = "https://your-endpoint-url-here"

prompt = "Tell me about AI"
prompt_template=f'''<human>: 
Please Complete the given function below according to the docstring: 
{prompt}
<bot>: 

'''

client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
                                  max_new_tokens=128,
                                  do_sample=True,
                                  temperature=0.7,
                                  top_p=0.95,
                                  top_k=40,
                                  repetition_penalty=1.1)

print(f"Model output: {response}")

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How to use this AWQ model from Python code

Install the necessary packages

Requires: AutoAWQ 0.1.1 or later

pip3 install autoawq

If you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:

pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .

You can then try the following example code

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_name_or_path = "TheBloke/Ziya-Coding-34B-v1.0-AWQ"

# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
                                          trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)

prompt = "Tell me about AI"
prompt_template=f'''<human>: 
Please Complete the given function below according to the docstring: 
{prompt}
<bot>: 

'''

print("\n\n*** Generate:")

tokens = tokenizer(
    prompt_template,
    return_tensors='pt'
).input_ids.cuda()

# Generate output
generation_output = model.generate(
    tokens,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    max_new_tokens=512
)

print("Output: ", tokenizer.decode(generation_output[0]))

"""
# Inference should be possible with transformers pipeline as well in future
# But currently this is not yet supported by AutoAWQ (correct as of September 25th 2023)
from transformers import pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])
"""

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Compatibility

The files provided are tested to work with:

• AutoAWQ
• vLLM
• Huggingface Text Generation Inference (TGI)

TGI merged AWQ support on September 25th, 2023: TGI PR #1054. Use the :latest Docker container until the next TGI release is made.

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Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

• Patreon: https://patreon.com/TheBlokeAI
• Ko-Fi: https://ko-fi.com/TheBlokeAI

Special thanks to: Aemon Algiz.

Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

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Original model card: Fengshenbang-LM's Ziya Coding 34B v1.0

Ziya-Coding-34B-v1.0

姜子牙系列模型

• Ziya-LLaMA-13B-v1.1
• Ziya-LLaMA-13B-v1
• Ziya-LLaMA-7B-Reward
• Ziya-LLaMA-13B-Pretrain-v1
• Ziya-BLIP2-14B-Visual-v1
• Ziya-Writing-LLaMa-13B-v1
• Ziya-Coding-15B-v1

简介 Brief Introduction

使用自然语言生成高质量的代码是大模型落地中的高频需求。今天，IDEA研究院封神榜团队正式开源最新的代码大模型Ziya-Coding-34B-v1.0，我们在HumanEval Pass@1的评测上，取得了75.5的好成绩，超过了GPT-4（67.0）的得分，也成为目前已知开源模型新高。封神榜团队正在为社区提供先进的大模型技术和经验，帮助生产和定制更多优秀垂类模型，推进大模型生态发展。

Generating high-quality code using natural language is a high-frequency demand in the deployment of large models. Today, the IDEA Research Institute's Fengshenbang team officially open-sourced the latest code model, Ziya-Coding-34B-v1.0. We achieved a good score of 75.5 on the HumanEval Pass@1 evaluation, surpassing the score of GPT-4 (67.0) and setting a new high for known open-source models. The Fengshenbang team is providing the community with advanced large model technology and experience, helping to produce and customize more excellent vertical models, and promoting the development of the large model ecosystem.

更多细节可以参考我们的公众号文章：

再创新高！姜子牙大模型开源代码大模型Ziya-Coding-34B-v1.0

姜子牙大模型系列 | 代码模型ziya-coding发布！低成本微调即可学会在专有场景编程

软件依赖

pip install torch==1.12.1 tokenizers==0.13.3 git+https://github.com/huggingface/transformers

模型信息 Model Information

在9月初，我们开源了基于StarCoder-15B的代码模型Ziya-Coding-15B-v1，我们将训练Ziya-Coding-15B-v1积累的训练经验迁移到了新版本的训练中。

我们收集并构造了约45万涵盖了几乎所有代码相关任务的指令数据进行第一阶段的微调，这其中包括约10万的中文指令和35万的英文指令，保证了数据的多样性，在构造数据时，我们充分利用了高质量的无指令代码数据，使用LLM生成对应的指令，扩充得到了更多高质量的代码指令数据。

同时实验过程中，我们注意到，代码指令的难度和正确性是训练代码模型成功的关键。因此，我们引入了第二阶段的精调。我们使用evol-instruct的方法生成了大量高难度多要求的代码指令数据，并利用代码编译器作为反馈，筛选出能够通过编译的代码。最后利用LLM生成单元测试进一步验证代码的正确性。我们最终筛选出了46k数据，在第一阶段模型的基础上，使用较低的学习率进行微调，最终得到了我们的Ziya-coding-34B-v1.0。

In early September, we open-sourced the code model Ziya-Coding-15B-v1 based on StarCoder-15B. The training experience accumulated in training Ziya-Coding-15B-v1 was transferred to the training of the new version.

We collected and constructed about 450,000 instruction data covering almost all code-related tasks for the first stage of fine-tuning. This includes about 100,000 Chinese instructions and 350,000 English instructions, ensuring data diversity. When constructing the data, we made full use of high-quality non-instructional code data, used LLM to generate corresponding instructions, and expanded to obtain more high-quality code instruction data.

During the experiment, we noticed that the difficulty and correctness of code instructions are key to the successful training of code models. Therefore, we introduced a second stage of fine-tuning. We used the evol-instruct method to generate a large amount of high-difficulty, multi-requirement code instruction data, and used a code compiler as feedback to filter out code that could pass compilation. Finally, we used LLM to generate unit tests to further verify the correctness of the code. We ultimately filtered out 46k data, and on the basis of the first-stage model, we fine-tuned it with a lower learning rate to finally obtain our Ziya-coding-34B-v1.0.

效果评估 Performance

其中，我们对微调数据集进行了去污处理，避免数据泄露，HumanEval的pass@1指标是贪婪生成的结果。

Prompt Format

"<human>: \nPlease Complete the given function below according to the docstring: \n{prompt}\n<bot>: \n"

In this process, we performed a decontamination process on the fine-tuning dataset to avoid data leakage. The pass@1 metric for HumanEval is based on the results of greedy generation.

<span id="jump"> 使用 Usage </span>

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

device = torch.device("cuda")
prompt = "写一段快速排序"
model = AutoModelForCausalLM.from_pretrained("IDEA-CCNL/Ziya-Coding-34B-v1.0", torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Ziya-Coding-34B-v1.0", use_fast=False)
input = f"<human>: \n{prompt}\n<bot>: \n"
input_ids = tokenizer(input, return_tensors="pt").input_ids.to(device)
generate_ids = model.generate(
            input_ids,
            max_new_tokens = 512, 
            do_sample = True, 
            top_p = 0.85, 
            temperature = 1.0,
            repetition_penalty = 1.0,
            eos_token_id = tokenizer.eos_token_id,
            pad_token_id = tokenizer.pad_token_id,
            )
output = tokenizer.batch_decode(generate_ids)[0]
print(output)

引用 Citation

如果您在您的工作中使用了我们的模型，可以引用我们的论文：

If you are using the resource for your work, please cite the our paper:

@article{fengshenbang,
  author    = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
  title     = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
  journal   = {CoRR},
  volume    = {abs/2209.02970},
  year      = {2022}
}

You can also cite our website:

欢迎引用我们的网站:

@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}