NVIDIA / TensorRT-Model-Optimizer

TensorRT Model Optimizer is a unified library of state-of-the-art model optimization techniques such as quantization, pruning, distillation, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed on NVIDIA GPUs.
https://nvidia.github.io/TensorRT-Model-Optimizer
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Bump transformers from 4.36.2 to 4.38.0 in /vlm_ptq #69

Closed dependabot[bot] closed 2 months ago

dependabot[bot] commented 2 months ago

Bumps transformers from 4.36.2 to 4.38.0.

Release notes

Sourced from transformers's releases.

v4.38: Gemma, Depth Anything, Stable LM; Static Cache, HF Quantizer, AQLM

New model additions

💎 Gemma 💎

Gemma is a new opensource Language Model series from Google AI that comes with a 2B and 7B variant. The release comes with the pre-trained and instruction fine-tuned versions and you can use them via AutoModelForCausalLM, GemmaForCausalLM or pipeline interface!

Read more about it in the Gemma release blogpost: https://hf.co/blog/gemma

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16)

input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)

You can use the model with Flash Attention, SDPA, Static cache and quantization API for further optimizations !

  • Flash Attention 2
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")

model = AutoModelForCausalLM.from_pretrained( "google/gemma-2b", device_map="auto", torch_dtype=torch.float16, attn_implementation="flash_attention_2" )

input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)

  • bitsandbytes-4bit
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")

model = AutoModelForCausalLM.from_pretrained( "google/gemma-2b", device_map="auto", load_in_4bit=True ) </tr></table>

... (truncated)

Commits
  • 08ab54a [ gemma] Adds support for Gemma 💎 (#29167)
  • 2de9314 [Maskformer] safely get backbone config (#29166)
  • 476957b 🚨 Llama: update rope scaling to match static cache changes (#29143)
  • 7a4bec6 Release: 4.38.0
  • ee3af60 Add support for fine-tuning CLIP-like models using contrastive-image-text exa...
  • 0996a10 Revert low cpu mem tie weights (#29135)
  • 15cfe38 [Core tokenization] add_dummy_prefix_space option to help with latest is...
  • efdd436 FIX [PEFT / Trainer ] Handle better peft + quantized compiled models (#29...
  • 5e95dca [cuda kernels] only compile them when initializing (#29133)
  • a7755d2 Generate: unset GenerationConfig parameters do not raise warning (#29119)
  • Additional commits viewable in compare view


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dependabot[bot] commented 2 months ago

OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting @dependabot ignore this major version or @dependabot ignore this minor version.

If you change your mind, just re-open this PR and I'll resolve any conflicts on it.