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--- |
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license: creativeml-openrail-m |
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language: |
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- en |
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tags: |
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- LLM |
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- tensorRT |
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- chatGLM |
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--- |
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## Model Card for lyraChatGLM |
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lyraChatGLM is currently the **fastest chatGLM-6B** available, as far as we know, it is also the **fisrt accelerated version of chatGLM-6B**. |
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The inference speed of lyraChatGLM is **10x** faster than the original version, and we're still working to 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: lyraChatGLM is mainly based on FasterTransformer compiled for SM=80 (A100, for example). |
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## Speed |
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### test environment |
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- device: Nvidia A100 40G |
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|version|speed| |
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|:-:|:-:| |
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|original|30 tokens/s| |
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|lyraChatGLM|310 tokens/s| |
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## Model Sources |
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- **Repository:** [https://huggingface.co/THUDM/chatglm-6b] |
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## Uses |
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```python |
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from transformers import AutoTokenizer |
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from faster_chat_glm import GLM6B, FasterChatGLM |
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tokenizer = AutoTokenizer.from_pretrained(chatglm6b_dir, trust_remote_code=True) |
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BATCH_SIZE = 8 |
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MAX_OUT_LEN = 50 |
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# prepare input |
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input_str = ["为什么我们需要对深度学习模型加速? ", ] * |
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inputs = tokenizer(input_str, return_tensors="pt", padding=True) |
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input_ids = inputs.input_ids.to('cuda:0') |
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# kernel for chat model. |
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kernel = GLM6B(plan_path="./models/glm6b-bs{BATCH_SIZE}.ftm", |
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batch_size=1, |
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num_beams=1, |
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use_cache=True, |
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num_heads=32, |
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emb_size_per_heads=128, |
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decoder_layers=28, |
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vocab_size=150528, |
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max_seq_len=MAX_OUT_LEN) |
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chat = FasterChatGLM(model_dir=chatglm6b_dir, kernel=kernel).half().cuda() |
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# generate |
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sample_output = chat.generate(inputs=input_ids, max_length=MAX_OUT_LEN) |
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# de-tokenize model output to text |
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res = tokenizer.decode(sample_output[0], skip_special_tokens=True) |
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print(res) |
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``` |
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## Demo output |
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### input |
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为什么我们需要对深度学习模型加速? 。 |
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### output |
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为什么我们需要对深度学习模型加速? 深度学习模型的训练需要大量计算资源,特别是在训练模型时,需要大量的内存、GPU(图形处理器)和其他计算资源。因此,训练深度学习模型需要一定的时间,并且如果模型不能快速训练,则可能会导致训练进度缓慢或无法训练。 |
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以下是一些原因我们需要对深度学习模型加速: |
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1. 训练深度神经网络需要大量的计算资源,特别是在训练深度神经网络时,需要更多的计算资源,因此需要更快的训练速度。 |
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## Environment |
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- hardware: Nvidia Ampere architecture (A100) or compatable |
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- docker image avaible: https://hub.docker.com/r/bigmoyan/lyra_aigc/tags |
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``` |
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docker pull bigmoyan/lyra_aigc:v0.1 |
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``` |
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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, Bin Wu}, |
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title = {lyaraChatGLM: Accelerating chatGLM by 10x+}, |
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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. |
