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76k Downloads Model Card for deberta-v3-base-prompt-injection
There is a newer version of the model - protectai/deberta-v3-base-prompt-injection-v2.
This model is a fine-tuned version of microsoft/deberta-v3-base on multiple combined datasets of prompt injections and normal prompts.
It aims to identify prompt injections, classifying inputs into two categories: 0 for no injection and 1 for injection detected.
It achieves the following results on the evaluation set:
Loss: 0.0010
Accuracy: 0.9999
Recall: 0.9997
Precision: 0.9998
F1: 0.9998
Model details
Fine-tuned by: Laiyer.ai
Model type: deberta-v3
Language(s) (NLP): English
License: Apache license 2.0
Finetuned from model: microsoft/deberta-v3-base
Intended Uses & Limitations
It aims to identify prompt injections, classifying inputs into two categories: 0 for no injection and 1 for injection detected.
The model's performance is dependent on the nature and quality of the training data. It might not perform well on text styles or topics not represented in the training set.
How to Get Started with the Model
Transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch
tokenizer = AutoTokenizer.from_pretrained("ProtectAI/deberta-v3-base-prompt-injection")
model = AutoModelForSequenceClassification.from_pretrained("ProtectAI/deberta-v3-base-prompt-injection")
classifier = pipeline(
"text-classification",
model=model,
tokenizer=tokenizer,
truncation=True,
max_length=512,
device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)
print(classifier("Your prompt injection is here"))
https://huggingface.co/protectai/deberta-v3-base-prompt-injection
There is a newer version of the model - protectai/deberta-v3-base-prompt-injection-v2.
This model is a fine-tuned version of microsoft/deberta-v3-base on multiple combined datasets of prompt injections and normal prompts.
It aims to identify prompt injections, classifying inputs into two categories: 0 for no injection and 1 for injection detected.
It achieves the following results on the evaluation set:
Loss: 0.0010
Accuracy: 0.9999
Recall: 0.9997
Precision: 0.9998
F1: 0.9998
Model details
Fine-tuned by: Laiyer.ai
Model type: deberta-v3
Language(s) (NLP): English
License: Apache license 2.0
Finetuned from model: microsoft/deberta-v3-base
Intended Uses & Limitations
It aims to identify prompt injections, classifying inputs into two categories: 0 for no injection and 1 for injection detected.
The model's performance is dependent on the nature and quality of the training data. It might not perform well on text styles or topics not represented in the training set.
How to Get Started with the Model
Transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch
tokenizer = AutoTokenizer.from_pretrained("ProtectAI/deberta-v3-base-prompt-injection")
model = AutoModelForSequenceClassification.from_pretrained("ProtectAI/deberta-v3-base-prompt-injection")
classifier = pipeline(
"text-classification",
model=model,
tokenizer=tokenizer,
truncation=True,
max_length=512,
device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)
print(classifier("Your prompt injection is here"))
https://huggingface.co/protectai/deberta-v3-base-prompt-injection
huggingface.co
protectai/deberta-v3-base-prompt-injection · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Model Card for deberta-v3-base-prompt-injection-v2
This model is a fine-tuned version of microsoft/deberta-v3-base specifically developed to detect and classify prompt injection attacks which can manipulate language models into producing unintended outputs.
Introduction
Prompt injection attacks manipulate language models by inserting or altering prompts to trigger harmful or unintended responses. The deberta-v3-base-prompt-injection-v2 model is designed to enhance security in language model applications by detecting these malicious interventions.
Model Details
Fine-tuned by: Protect AI
Model type: deberta-v3-base
Language(s) (NLP): English
License: Apache License 2.0
Finetuned from model: microsoft/deberta-v3-base
Intended Uses
This model classifies inputs into benign (0) and injection-detected (1).
Limitations
deberta-v3-base-prompt-injection-v2 is highly accurate in identifying prompt injections in English. It does not detect jailbreak attacks or handle non-English prompts, which may limit its applicability in diverse linguistic environments or against advanced adversarial techniques.
Additionally, we do not recommend using this scanner for system prompts, as it produces false-positives.
https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2
This model is a fine-tuned version of microsoft/deberta-v3-base specifically developed to detect and classify prompt injection attacks which can manipulate language models into producing unintended outputs.
Introduction
Prompt injection attacks manipulate language models by inserting or altering prompts to trigger harmful or unintended responses. The deberta-v3-base-prompt-injection-v2 model is designed to enhance security in language model applications by detecting these malicious interventions.
Model Details
Fine-tuned by: Protect AI
Model type: deberta-v3-base
Language(s) (NLP): English
License: Apache License 2.0
Finetuned from model: microsoft/deberta-v3-base
Intended Uses
This model classifies inputs into benign (0) and injection-detected (1).
Limitations
deberta-v3-base-prompt-injection-v2 is highly accurate in identifying prompt injections in English. It does not detect jailbreak attacks or handle non-English prompts, which may limit its applicability in diverse linguistic environments or against advanced adversarial techniques.
Additionally, we do not recommend using this scanner for system prompts, as it produces false-positives.
https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2
huggingface.co
protectai/deberta-v3-base-prompt-injection-v2 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Clip-Roco_Version2_v2
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.8034
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 3
eval_batch_size: 3
seed: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 5
num_epochs: 5
https://huggingface.co/MohamedAhmedAE/Clip-Roco_Version2_v2
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.8034
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 3
eval_batch_size: 3
seed: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 5
num_epochs: 5
https://huggingface.co/MohamedAhmedAE/Clip-Roco_Version2_v2
huggingface.co
MohamedAhmedAE/Clip-Roco_Version2_v2 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Model tree for hugging-quants/Meta-Llama-3.1-405B-BNB-NF4-BF16
Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
This repository contains meta-llama/Meta-Llama-3.1-405B quantized using bitsandbytes from BF16 down to NF4 with a block size of 64, and storage type torch.bfloat16.
Model Usage
In order to run the inference with Llama 3.1 405B BNB in NF4, around 220 GiB of VRAM are needed only for loading the model checkpoint, without including the KV cache or the CUDA graphs, meaning that there should be a bit over that VRAM available.
In order to use the current quantized model, support is offered for different solutions as transformers, or text-generation-inference.
🤗 transformers
In order to run the inference with Llama 3.1 405B BNB in NF4, both torch and bitsandbytes need to be installed as:
pip install "torch>=2.0.0" bitsandbytes --upgrade
Then, the latest version of transformers need to be installed, being 4.43.0 or higher, as:
pip install "transformers[accelerate]>=4.43.0" --upgrade
To run the inference on top of Llama 3.1 405B BNB in NF4 precision, the model can be instantiated as any other causal language modeling model via AutoModelForCausalLM and run the inference normally.https://huggingface.co/hugging-quants/Meta-Llama-3.1-405B-BNB-NF4-BF16
Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
This repository contains meta-llama/Meta-Llama-3.1-405B quantized using bitsandbytes from BF16 down to NF4 with a block size of 64, and storage type torch.bfloat16.
Model Usage
In order to run the inference with Llama 3.1 405B BNB in NF4, around 220 GiB of VRAM are needed only for loading the model checkpoint, without including the KV cache or the CUDA graphs, meaning that there should be a bit over that VRAM available.
In order to use the current quantized model, support is offered for different solutions as transformers, or text-generation-inference.
🤗 transformers
In order to run the inference with Llama 3.1 405B BNB in NF4, both torch and bitsandbytes need to be installed as:
pip install "torch>=2.0.0" bitsandbytes --upgrade
Then, the latest version of transformers need to be installed, being 4.43.0 or higher, as:
pip install "transformers[accelerate]>=4.43.0" --upgrade
To run the inference on top of Llama 3.1 405B BNB in NF4 precision, the model can be instantiated as any other causal language modeling model via AutoModelForCausalLM and run the inference normally.https://huggingface.co/hugging-quants/Meta-Llama-3.1-405B-BNB-NF4-BF16
huggingface.co
hugging-quants/Meta-Llama-3.1-405B-BNB-NF4-BF16 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
2024 The Hugging-quants/Meta-Llama-3.1-405B-BNB-NF4
Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
This repository contains meta-llama/Meta-Llama-3.1-405B quantized using bitsandbytes from BF16 down to NF4 with a block size of 64.
🤗 transformers
In order to run the inference with Llama 3.1 405B BNB in NF4, both torch and bitsandbytes need to be installed as:
pip install "torch>=2.0.0" bitsandbytes --upgrade
Then, the latest version of transformers need to be installed, being 4.43.0 or higher, as:
pip install "transformers[accelerate]>=4.43.0" --upgrade
To run the inference on top of Llama 3.1 405B BNB in NF4 precision, the model can be instantiated as any other causal language modeling model via AutoModelForCausalLM and run the inference normally.https://huggingface.co/hugging-quants/Meta-Llama-3.1-405B-BNB-NF4
Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
This repository contains meta-llama/Meta-Llama-3.1-405B quantized using bitsandbytes from BF16 down to NF4 with a block size of 64.
🤗 transformers
In order to run the inference with Llama 3.1 405B BNB in NF4, both torch and bitsandbytes need to be installed as:
pip install "torch>=2.0.0" bitsandbytes --upgrade
Then, the latest version of transformers need to be installed, being 4.43.0 or higher, as:
pip install "transformers[accelerate]>=4.43.0" --upgrade
To run the inference on top of Llama 3.1 405B BNB in NF4 precision, the model can be instantiated as any other causal language modeling model via AutoModelForCausalLM and run the inference normally.https://huggingface.co/hugging-quants/Meta-Llama-3.1-405B-BNB-NF4
huggingface.co
hugging-quants/Meta-Llama-3.1-405B-BNB-NF4 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Vintern-1B ❄️ (Viet-InternVL2-1B) [🤗 HF Demo] - The LLaVA 🌋 Challenger
We are excited to introduce Vintern-1B the Vietnamese 🇻🇳 multimodal model that combines the advanced Vietnamese language model Qwen2-0.5B-Instruct with the latest visual model, InternViT-300M-448px, CVPR 2024. This model excels in tasks such as OCR-VQA, Doc-VQA, and Chart-VQA,... With only 1 billion parameters, it is 4096 context length finetuned from the InternVL2-1B model on over 1.5 million specialized image-question-answer pairs for optical character recognition 🔍, text recognition 🔤, document extraction 📑, and more. The model can be integrated into various on-device applications 📱, demonstrating its versatility and robust capabilities.
Vintern-1B is a multimodal large language model series, featuring models of various sizes. For each size, we release instruction-tuned models optimized for multimodal tasks. Vintern-1B consists of InternViT-300M-448px, an MLP projector, and Qwen2-0.5B-Instruct.
Training details 📚
The fine-tuning dataset was meticulously sampled in part from the following datasets:
Viet-OCR-VQA
Viet-Doc-VQA
Viet-Doc-VQA-II
Benchmarks 📈
Since there are still many different metrics that need to be tested, we chose a quick and simple metric first to guide the development of our model. Our metric is inspired by Lavy's paper. For the time being, we are using GPT-4 to evaluate the quality of answers on two datasets: OpenViVQA and ViTextVQA. Detailed results can be found at the provided Here. The inputs are images, questions, labels, and predicted answers. The model will return a score from 0 to 10 for the corresponding answer quality. The results table is shown below.https://huggingface.co/5CD-AI/Viet-InternVL2-1B
We are excited to introduce Vintern-1B the Vietnamese 🇻🇳 multimodal model that combines the advanced Vietnamese language model Qwen2-0.5B-Instruct with the latest visual model, InternViT-300M-448px, CVPR 2024. This model excels in tasks such as OCR-VQA, Doc-VQA, and Chart-VQA,... With only 1 billion parameters, it is 4096 context length finetuned from the InternVL2-1B model on over 1.5 million specialized image-question-answer pairs for optical character recognition 🔍, text recognition 🔤, document extraction 📑, and more. The model can be integrated into various on-device applications 📱, demonstrating its versatility and robust capabilities.
Vintern-1B is a multimodal large language model series, featuring models of various sizes. For each size, we release instruction-tuned models optimized for multimodal tasks. Vintern-1B consists of InternViT-300M-448px, an MLP projector, and Qwen2-0.5B-Instruct.
Training details 📚
The fine-tuning dataset was meticulously sampled in part from the following datasets:
Viet-OCR-VQA
Viet-Doc-VQA
Viet-Doc-VQA-II
Benchmarks 📈
Since there are still many different metrics that need to be tested, we chose a quick and simple metric first to guide the development of our model. Our metric is inspired by Lavy's paper. For the time being, we are using GPT-4 to evaluate the quality of answers on two datasets: OpenViVQA and ViTextVQA. Detailed results can be found at the provided Here. The inputs are images, questions, labels, and predicted answers. The model will return a score from 0 to 10 for the corresponding answer quality. The results table is shown below.https://huggingface.co/5CD-AI/Viet-InternVL2-1B
huggingface.co
5CD-AI/Viet-InternVL2-1B · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
merve/sam2-hiera-small
SAM2-Hiera-small
This repository contains small variant of SAM2 model. SAM2 is the state-of-the-art mask generation model released by Meta.
Usage
You can use it like below. First install packaged version of SAM2.
pip install samv2 huggingface_hub
Each model requires different classes to infer.
For prompting:
Rred by: https://huggingface.co/merve/sam2-hiera-small
SAM2-Hiera-small
This repository contains small variant of SAM2 model. SAM2 is the state-of-the-art mask generation model released by Meta.
Usage
You can use it like below. First install packaged version of SAM2.
pip install samv2 huggingface_hub
Each model requires different classes to infer.
For prompting:
Rred by: https://huggingface.co/merve/sam2-hiera-small
huggingface.co
merve/sam2-hiera-small · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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