id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
hugging_face/quantization_172_0.txt | [ < source >
](https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/quantizers/base.py#L184) | |
hugging_face/quantization_25_0.txt | [ Overview ](/docs/transformers/en/performance) [ Quantization
](/docs/transformers/en/quantization) | |
hugging_face/quantization_169_0.txt | * **model** ( ` ~transformers.PreTrainedModel ` ) — The model to quantize
* **torch_dtype** ( ` torch.dtype ` ) — The dtype passed in ` from_pretrained ` method. | |
hugging_face/quantization_70_0.txt | ( ) | |
hugging_face/quantization_113_0.txt | ## BitsAndBytesConfig | |
hugging_face/hfquantizer_80_0.txt | [ < > Update on GitHub
](https://github.com/huggingface/transformers/blob/main/docs/source/en/hf_quantizer.md) | |
hugging_face/quantization_20_0.txt | Generation | |
hugging_face/quantization_99_0.txt | * **bits** ( ` int ` ) — The number of bits to quantize to, supported numbers are (2, 3, 4, 8).
* **tokenizer** ( ` str ` or ` PreTrainedTokenizerBase ` , _optional_ ) — The tokenizer used to process the dataset. You can pass either:
* A custom tokenizer object.
* A string, the _model id_ of a predefined tokenizer host... | |
hugging_face/495_65_0.txt | Hey,
I'm trying to use a quantized model due to memory issue.
We usually load the model like this, | |
hugging_face/495_136_0.txt |
###### After quantization
# +-----------------------------------------------------------------------------------------+
# | NVIDIA-SMI 550.54.14 Driver Version: 550.54.14 CUDA Version: 12.4 |
# |-----------------------------------------+------------------------+----------------------+
# | GPU Na... | |
hugging_face/495_42_0.txt | * [ Notifications ](/login?return_to=%2Fstanfordnlp%2Fdspy)
* [ Fork 811 ](/login?return_to=%2Fstanfordnlp%2Fdspy)
* [ Star 10.8k ](/login?return_to=%2Fstanfordnlp%2Fdspy) | |
hugging_face/hfquantizer_57_0.txt | to get started | |
hugging_face/495_20_0.txt | * [ Topics ](https://github.com/topics)
* [ Trending ](https://github.com/trending)
* [ Collections ](https://github.com/collections) | |
hugging_face/quantization_124_0.txt | ( ) | |
hugging_face/495_153_0.txt | [ 
](/thomasahle) [
](/learnbott) [
](/koshyviv) [
 [ Utilities for pipelines
](/docs/transformers/en/internal/pipelines_utils) [ Utilities for Tokenizers
](/docs/transformers/en/internal/tokenization_utils) [ Utilities for Trainer
](/docs/transformers/en/internal/trainer_utils) [ Utilities f... | |
hugging_face/495_119_0.txt | Sorry, something went wrong. | |
hugging_face/hugging_face_4_2.txt | berger, Johannes
> and Gurevych, Iryna",
> booktitle = "Proceedings of the 2021 Conference of the North American
> Chapter of the Association for Computational Linguistics: Human Language
> Technologies",
> month = jun,
> year = "2021",
> address = "Online",
> publisher = "Association for ... | |
hugging_face/quantization_26_0.txt | Efficient training techniques | |
hugging_face/quantization_47_0.txt | Internal Helpers | |
hugging_face/hfquantizer_17_0.txt | Audio | |
hugging_face/495_74_0.txt | Copy link | |
hugging_face/quantization_185_0.txt | ( device_map : Optional ) | |
hugging_face/modelquantizationwit_56_0.txt | The integration also recommends using the nested quantization technique for
even greater memory efficiency without sacrificing performance. This technique
has proven beneficial, especially when fine-tuning large models: | |
hugging_face/hfquantizer_16_0.txt | Natural Language Processing | |
hugging_face/modelquantizationwit_42_0.txt | You can even check the memory footprint of your model using the `
get_memory_footprint ` method: | |
hugging_face/quantization_125_0.txt | Returns ` True ` if the model is quantizable, ` False ` otherwise. | |
hugging_face/495_27_0.txt | # Provide feedback | |
hugging_face/quantization_120_0.txt | This replaces ` load_in_8bit ` or ` load_in_4bit ` therefore both options are
mutually exclusive. | |
hugging_face/495_111_0.txt | [ 
](/ujjawal-ti) | |
hugging_face/modelquantizationwit_72_0.txt | ## Fine-Tuning a Model Loaded in 8-bit | |
hugging_face/495_78_0.txt | --- | |
hugging_face/quantization_10_0.txt | [ ](https://github.com/huggingface/transformers) | |
hugging_face/quantization_76_0.txt | Parameters | |
hugging_face/quantization_91_0.txt | [ < source >
](https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/utils/quantization_config.py#L695) | |
hugging_face/quantization_147_0.txt | #### adjust_max_memory | |
hugging_face/495_133_0.txt | dspy.settings.configure(lm=llm) | |
hugging_face/hfquantizer_21_0.txt | Prompting | |
hugging_face/modelquantizationwit_65_0.txt | There are additional techniques and configurations to consider: | |
hugging_face/quantization_159_0.txt | ( model : PreTrainedModel param_value : torch.Tensor param_name : str
state_dict : Dict **kwargs ) | |
hugging_face/quantization_126_0.txt | #### post_init | |
hugging_face/modelquantizationwit_63_0.txt | In this case, you don’t need to specify the ` load_in_8bit=True ` argument,
but you must have both Bitsandbytes and Accelerate library installed. | |
hugging_face/modelquantizationwit_112_0.txt | Text to speech | |
hugging_face/modelquantizationwit_80_0.txt | [ Generative Ai Tools ](/tag/generative-ai-tools?source=post_page-----
b4c9983e8996---------------generative_ai_tools-----------------) | |
hugging_face/495_14_0.txt | Resources | |
hugging_face/hugging_face_1_0.txt | [  Hugging
Face ](/)
* [ Models ](/models)
* [ Datasets ](/datasets)
* [ Spaces ](/spaces)
* [ Posts ](/posts)
* [ Docs ](/docs)
* Solutions
* [ Pricing ](/pricing)
* * * * *
* [ Log In ](/login)
* [ Sign Up ](/join)
Hub docu... | |
hugging_face/quantization_207_0.txt | Quantization Quanto Config Aqlm Config Awq Config GPTQ Config Bits
And Bytes Config Hf Quantizer | |
hugging_face/quantization_36_0.txt | Main Classes | |
hugging_face/495_109_0.txt | All reactions | |
hugging_face/modelquantizationwit_26_0.txt | Install latest accelerate from source: | |
hugging_face/quantization_116_0.txt | ( load_in_8bit = False load_in_4bit = False llm_int8_threshold = 6.0
llm_int8_skip_modules = None llm_int8_enable_fp32_cpu_offload = False
llm_int8_has_fp16_weight = False bnb_4bit_compute_dtype = None
bnb_4bit_quant_type = 'fp4' bnb_4bit_use_double_quant = False
bnb_4bit_quant_storage = None **kwargs ... | |
hugging_face/495_64_0.txt | **[ ujjawal-ti ](/ujjawal-ti) ** commented Feb 29, 2024 | |
hugging_face/modelquantizationwit_31_0.txt | Hugging Face and Bitsandbytes Integration Uses | |
hugging_face/modelquantizationwit_52_0.txt | from transformers import BitsAndBytesConfig | |
hugging_face/495_75_0.txt | ### | |
hugging_face/hfquantizer_64_0.txt | Copied | |
hugging_face/495_52_0.txt | # How to use any quantized huggingface transformers model #495 | |
hugging_face/quantization_145_0.txt | Abstract class of the HuggingFace quantizer. Supports for now quantizing HF
transformers models for inference and/or quantization. This class is used only
for transformers.PreTrainedModel.from_pretrained and cannot be easily used
outside the scope of that method yet. | |
hugging_face/modelquantizationwit_47_0.txt | You can modify the data type used during computation by setting the `
bnb_4bit_compute_dtype ` to a different value, such as ` torch.bfloat16 ` .
This can result in speed improvements in specific scenarios. Here's an
example: | |
hugging_face/495_131_0.txt | config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["k_proj", "v_proj", "q_proj", "o_proj"],
lora_dropout=0.05,
bias="all",
task_type="CAUSAL_LM",
) | |
hugging_face/modelquantizationwit_40_0.txt | model_id = "bigscience/bloom-1b7" | |
hugging_face/quantization_166_0.txt | [ < source >
](https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/quantizers/base.py#L112) | |
hugging_face/hfquantizer_27_0.txt | [ Methods and tools for efficient training on a single GPU
](/docs/transformers/main/en/perf_train_gpu_one) [ Multiple GPUs and
parallelism ](/docs/transformers/main/en/perf_train_gpu_many) [ Fully Sharded
Data Parallel ](/docs/transformers/main/en/fsdp) [ DeepSpeed
](/docs/transformers/main/en/deepspeed) [ Efficient... | |
hugging_face/quantization_206_0.txt | [ ← Processors ](/docs/transformers/en/main_classes/processors) [ Tokenizer
→ ](/docs/transformers/en/main_classes/tokenizer) | |
hugging_face/quantization_57_0.txt | # Quantization | |
hugging_face/modelquantizationwit_2_0.txt | [ Sign in
](/m/signin?operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40rakeshrajpurohit%2Fmodel-
quantization-with-hugging-face-transformers-and-bitsandbytes-
integration-b4c9983e8996&source=post_page---two_column_layout_nav
-----------------------global_nav-----------) | |
hugging_face/495_18_0.txt | * [ The ReadME Project GitHub community articles ](https://github.com/readme) | |
hugging_face/quantization_101_0.txt | #### from_dict_optimum | |
hugging_face/modelquantizationwit_32_0.txt | ## Loading a Model in 4-bit Quantization | |
hugging_face/quantization_81_0.txt | ( ) | |
hugging_face/495_130_0.txt | quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype="float16",
)
llm.model=AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config) | |
hugging_face/quantization_95_0.txt | ### class transformers. GPTQConfig | |
hugging_face/quantization_178_0.txt | [ < source >
](https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/quantizers/base.py#L168) | |
hugging_face/modelquantizationwit_114_0.txt | Teams | |
hugging_face/495_124_0.txt | **[ learnbott ](/learnbott) ** commented Apr 24, 2024 • | |
hugging_face/quantization_170_0.txt | returns dtypes for modules that are not quantized - used for the computation
of the device_map in case one passes a str as a device_map. The method will
use the ` modules_to_not_convert ` that is modified in `
_process_model_before_weight_loading ` . | |
hugging_face/modelquantizationwit_21_0.txt | This blog post explores the integration of Hugging Face’s Transformers library
with the Bitsandbytes library, which simplifies the process of model
quantization, making it more accessible and user-friendly. | |
hugging_face/quantization_53_0.txt | Faster examples with accelerated inference | |
hugging_face/quantization_197_0.txt | ( torch_dtype : torch.dtype ) | |
hugging_face/quantization_142_0.txt | ### class transformers.quantizers. HfQuantizer | |
hugging_face/modelquantizationwit_18_0.txt | Listen | |
hugging_face/quantization_160_0.txt | checks if a loaded state_dict component is part of quantized param + some
validation; only defined if requires_parameters_quantization == True for
quantization methods that require to create a new parameters for quantization. | |
hugging_face/hfquantizer_9_0.txt | main v4.40.1 v4.39.3 v4.38.2 v4.37.2 v4.36.1 v4.35.2 v4.34.1 v4.33.3
v4.32.1 v4.31.0 v4.30.0 v4.29.1 v4.28.1 v4.27.2 v4.26.1 v4.25.1
v4.24.0 v4.23.1 v4.22.2 v4.21.3 v4.20.1 v4.19.4 v4.18.0 v4.17.0
v4.16.2 v4.15.0 v4.14.1 v4.13.0 v4.12.5 v4.11.3 v4.10.1 v4.9.2 v4.8.2
v4.7.0 v4.6.0 v4.5.1 ... | |
hugging_face/495_22_0.txt | Search or jump to... | |
hugging_face/hugging_face_1_1.txt | b documentation
The Hugging Face Hub is a platform with over 350k models, 75k datasets, and
150k demo apps (Spaces), all open source and publicly available, in an online
platform where people can easily collaborate and build ML together. The Hub
works as a central place where anyone can explore, experiment, collaborat... | |
hugging_face/modelquantizationwit_62_0.txt | model = AutoModelForCausalLM.from_pretrained("model_name", device_map="auto") | |
hugging_face/495_47_0.txt | **Have a question about this project?** Sign up for a free GitHub account to
open an issue and contact its maintainers and the community. | |
hugging_face/495_16_0.txt | * Open Source | |
hugging_face/quantization_0_0.txt | [  Hugging
Face ](/) | |
hugging_face/hugging_face_4_1.txt | trained models ](docs/pretrained_models.html) tuned for various tasks.
Further, it is easy to [ fine-tune your own models
](docs/training/overview.html) .
# Installation ¶
You can install it using pip:
pip install -U sentence-transformers
We recommend **Python 3.8** or higher, and at least **... | |
hugging_face/hfquantizer_75_0.txt | 4. Write the ` validate_environment ` and ` update_torch_dtype ` methods. These methods are called before creating the quantized model to ensure users use the right configuration. You can have a look at how this is done on other quantizers. | |
hugging_face/modelquantizationwit_90_0.txt | Follow | |
hugging_face/495_23_0.txt | # Search code, repositories, users, issues, pull requests... | |
hugging_face/495_152_0.txt | 5 participants | |
hugging_face/hfquantizer_6_0.txt | # Transformers | |
hugging_face/495_72_0.txt | All reactions | |
hugging_face/quantization_78_0.txt | This is a wrapper class about ` aqlm ` parameters. | |
hugging_face/495_49_0.txt | By clicking “Sign up for GitHub”, you agree to our [ terms of service
](https://docs.github.com/terms) and [ privacy statement
](https://docs.github.com/privacy) . We’ll occasionally send you account
related emails. | |
hugging_face/hfquantizer_23_0.txt | [ Use fast tokenizers from 🤗 Tokenizers
](/docs/transformers/main/en/fast_tokenizers) [ Run inference with
multilingual models ](/docs/transformers/main/en/multilingual) [ Use model-
specific APIs ](/docs/transformers/main/en/create_a_model) [ Share a custom
model ](/docs/transformers/main/en/custom_models) [ Templ... |
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