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--- |
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license: creativeml-openrail-m |
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language: en |
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tags: |
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- LLM |
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- ChatGLM |
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--- |
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## Breakings! |
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**We know what you want, and here they are!** |
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- Newly released lyraChatGLM model, suitable for Ampere(A100/A10) as well as Volta(V100) |
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- lyraChatGLM has been further optimized, reaches **9000tokens/s** on A100 and **3900 tokens/s** on V100, about **5.5x** faster than original version(2023/6/1). |
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- The memory usage was optimized too, now we can set batch_size up to **256** on A100! |
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**Note that the code was fully updated too, you need to use new API, see `Uses` below** |
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## Model Card for lyraChatGLM |
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lyraChatGLM is currently the **fastest ChatGLM-6B** available. To the best of our knowledge, it is the **first accelerated version of ChatGLM-6B**. |
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The inference speed of lyraChatGLM has achieved **300x** acceleration upon the ealry original version. We are still working hard to further improve the performance. |
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Among its main features are: |
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- weights: original ChatGLM-6B weights released by THUDM. |
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- device: Nvidia GPU with Amperer architecture or Volta architecture (A100, A10, V100...). |
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- batch_size: compiled with dynamic batch size, maximum depends on device. |
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## Speed |
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- orginal version(fixed batch infer): commit id 1d240ba |
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### test on A100 40G |
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|version|max_batch_size|max_speed| |
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|:-:|:-:|:-:| |
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|original|1|30 tokens/s| |
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|original(fxied batch infer)|192|1638.52 toekns/s| |
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|lyraChatGLM(current)|256|9082.60+ tokens/s| |
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### test on V100 |
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|version|max_batch_size|max_speed| |
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|:-:|:-:|:-:| |
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|original|1|17.83 tokens/s| |
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|original(fxied batch infer)|128|992.20 toekns/s| |
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|lyraChatGLM(current)|192|3911.45+ tokens/s| |
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## Model Sources |
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- **Repository:** https://huggingface.co/THUDM/chatglm-6b |
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## Docker Environment |
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- **docker image available** at [https://hub.docker.com/repository/docker/bigmoyan/lyrallm/general], pull image by: |
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``` |
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docker pull bigmoyan/lyrallm:v0.1 |
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``` |
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## Uses |
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```python |
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from lyraChatGLM import LyraChatGLM6B |
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model_path = "./models/1-gpu-fp16.h5" |
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tokenizer_path = "./models" |
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data_type = "fp16" |
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int8_mode = 0 |
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max_output_length = 150 |
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arch = "Ampere" # Ampere or Volta |
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model = LyraChatGLM6B(model_path, tokenizer_path, data_type, int8_mode, arch) |
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prompt = "列出3个不同的机器学习算法，并说明它们的适用范围." |
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test_batch_size = 256 |
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prompts = [prompt, ] |
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# If you want to get different output in same batch, you can set do_sample to True |
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output_texts = model.generate(prompts, output_length=max_output_length,top_k=30, top_p=0.85, temperature=0.35, repetition_penalty=1.2, do_sample=False) |
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print(output_texts) |
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``` |
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## Demo output |
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### input |
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列出3个不同的机器学习算法，并说明它们的适用范围. |
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### output |
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以下是三个常见的机器学习算法及其适用范围: |
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1. 决策树(Decision Tree):决策树是一种基于分类和回归问题的朴素贝叶斯模型。它通过构建一系列逐步分裂的分支来预测结果。适用于那些具有简单特征、大量数据且数据集大小在可接受范围内的情况。 |
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2. 随机森林(Random Forest):随机森林是一种集成学习算法,由多个决策树组成。它的优点是能够处理大规模数据和高维度的特征。适用于需要对多个变量进行建模的场景,例如医疗诊断、金融风险评估等。 |
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3. 支持向量机(Support Vector Machine):支持向量机是一种监督学习方法,通常用于分类问题。它可以处理高维数据,并且具有较高的准确性。适用于需要对高维数据进行分类或回归的问题,例如图像识别、自然语言处理等。 |
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## Citation |
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``` bibtex |
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@Misc{lyraChatGLM2023, |
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author = {Kangjian Wu, Zhengtao Wang, Yibo Lu, Bin Wu}, |
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title = {lyraChatGLM: Accelerating ChatGLM by 5.5x+}, |
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howpublished = {\url{https://huggingface.co/TMElyralab/lyraChatGLM}}, |
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year = {2023} |
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} |
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``` |
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## Report bug |
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- start a discussion to report any bugs!--> https://huggingface.co/TMElyralab/lyraChatGLM/discussions |
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- report bug with a `[bug]` mark in the title. |
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