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chore(deps): update dependency transformers to v4.41.1 #1234

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@renovate renovate bot commented Apr 28, 2024 •
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Mend Renovate

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
transformers ==4.38.0 -> ==4.41.1 age adoption passing confidence

Release Notes

huggingface/transformers (transformers)

v4.41.1: Fix PaliGemma finetuning, and some small bugs

Compare Source

Release v4.41.1

Fix PaliGemma finetuning:

The causal mask and label creation was causing label leaks when training. Kudos to @​probicheaux for finding and reporting!

Other fixes:

Reverted huggingface/transformers@4ab7a28

v4.41.0: : Phi3, JetMoE, PaliGemma, VideoLlava, Falcon2, FalconVLM & GGUF support

Compare Source

New models

Phi3

The Phi-3 model was proposed in Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone by Microsoft.

TLDR; Phi-3 introduces new ROPE scaling methods, which seems to scale fairly well! A 3b and a
Phi-3-mini is available in two context-length variants—4K and 128K tokens. It is the first model in its class to support a context window of up to 128K tokens, with little impact on quality.

image
JetMoE

JetMoe-8B is an 8B Mixture-of-Experts (MoE) language model developed by Yikang Shen and MyShell. JetMoe project aims to provide a LLaMA2-level performance and efficient language model with a limited budget. To achieve this goal, JetMoe uses a sparsely activated architecture inspired by the ModuleFormer. Each JetMoe block consists of two MoE layers: Mixture of Attention Heads and Mixture of MLP Experts. Given the input tokens, it activates a subset of its experts to process them. This sparse activation schema enables JetMoe to achieve much better training throughput than similar size dense models. The training throughput of JetMoe-8B is around 100B tokens per day on a cluster of 96 H100 GPUs with a straightforward 3-way pipeline parallelism strategy.

image
PaliGemma

PaliGemma is a lightweight open vision-language model (VLM) inspired by PaLI-3, and based on open components like the SigLIP vision model and the Gemma language model. PaliGemma takes both images and text as inputs and can answer questions about images with detail and context, meaning that PaliGemma can perform deeper analysis of images and provide useful insights, such as captioning for images and short videos, object detection, and reading text embedded within images.

More than 120 checkpoints are released see the collection here !

image
VideoLlava

Video-LLaVA exhibits remarkable interactive capabilities between images and videos, despite the absence of image-video pairs in the dataset.

💡 Simple baseline, learning united visual representation by alignment before projection
With the binding of unified visual representations to the language feature space, we enable an LLM to perform visual reasoning capabilities on both images and videos simultaneously.
🔥 High performance, complementary learning with video and image
Extensive experiments demonstrate the complementarity of modalities, showcasing significant superiority when compared to models specifically designed for either images or videos.

image
Falcon 2 and FalconVLM:
image

Two new models from TII-UAE! They published a blog-post with more details! Falcon2 introduces parallel mlp, and falcon VLM uses the Llava framework

GGUF from_pretrained support

image

You can now load most of the GGUF quants directly with transformers' from_pretrained to convert it to a classic pytorch model. The API is simple:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
filename = "tinyllama-1.1b-chat-v1.0.Q6_K.gguf"

tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename)

We plan more closer integrations with llama.cpp / GGML ecosystem in the future, see: https://github.com/huggingface/transformers/issues/27712 for more details

Quantization

New quant methods

In this release we support new quantization methods: HQQ & EETQ contributed by the community. Read more about how to quantize any transformers model using HQQ & EETQ in the dedicated documentation section

dequantize API for bitsandbytes models

In case you want to dequantize models that have been loaded with bitsandbytes, this is now possible through the dequantize API (e.g. to merge adapter weights)

API-wise, you can achieve that with the following:

from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer

model_id = "facebook/opt-125m"

model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=BitsAndBytesConfig(load_in_4bit=True))
tokenizer = AutoTokenizer.from_pretrained(model_id)

model.dequantize()

text = tokenizer("Hello my name is", return_tensors="pt").to(0)

out = model.generate(**text)
print(tokenizer.decode(out[0]))

Generation updates

SDPA support

Improved Object Detection

Addition of fine-tuning script for object detection models

Interpolation of embeddings for vision models

Add interpolation of embeddings. This enables predictions from pretrained models on input images of sizes different than those the model was originally trained on. Simply pass interpolate_pos_embedding=True when calling the model.

Added for: BLIP, BLIP 2, InstructBLIP, SigLIP, ViViT

import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration

image = Image.open(requests.get("https://huggingface.co/hf-internal-testing/blip-test-image/resolve/main/demo.jpg", stream=True).raw)
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained(
    "Salesforce/blip2-opt-2.7b", 
    torch_dtype=torch.float16
).to("cuda")
inputs = processor(images=image, size={"height": 500, "width": 500}, return_tensors="pt").to("cuda")

predictions = model(**inputs, interpolate_pos_encoding=True)

### Generated text: "a woman and dog on the beach"
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()

🚨 might be breaking

Cleanups
Not breaking but important for Llama tokenizers

Fixes

Configuration

📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

  • If you want to rebase/retry this PR, check this box

This PR has been generated by Mend Renovate. View repository job log here.

@renovate renovate bot force-pushed the renovate/transformers-4.x branch from 12e6b15 to 4c0b785 Compare May 6, 2024 16:45
@renovate renovate bot changed the title Update dependency transformers to v4.40.1 Update dependency transformers to v4.40.2 May 6, 2024
@renovate renovate bot changed the title Update dependency transformers to v4.40.2 chore(deps): update dependency transformers to v4.40.2 May 12, 2024
@renovate renovate bot force-pushed the renovate/transformers-4.x branch from 4c0b785 to cbc929a Compare May 17, 2024 20:41
@renovate renovate bot changed the title chore(deps): update dependency transformers to v4.40.2 chore(deps): update dependency transformers to v4.41.0 May 17, 2024
@renovate renovate bot force-pushed the renovate/transformers-4.x branch from cbc929a to 4062dfd Compare May 22, 2024 21:22
@renovate renovate bot changed the title chore(deps): update dependency transformers to v4.41.0 chore(deps): update dependency transformers to v4.41.1 May 22, 2024
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sonarcloud bot commented May 22, 2024

Quality Gate Passed Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
No data about Coverage
No data about Duplication

See analysis details on SonarCloud

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