[!Note]
Experimetal requantization!! I wanted to test if the QAT model requantized performs better than the bf16 model quantized to the same bit level.
I have created a imatrix files from the google original QAT Q4_0 quantized model. This imatrix is then used to recompress the model to lower bit quants
Please leave feedback.
I tested with the 4b model quantized from bf16 and one requantized from the QAT Q4_0 model. Both quantized with same tensor quants
Asking both models to : write some .net code to test if a website is using quantum safe encryption
And then asking Deepseek-R1 to evaluate :
Evaluation of the Two Models' Outputs
Both models attempted to solve the problem of detecting quantum-safe encryption, but the QAT q4_0 model's code is significantly better for the following reasons:
Technical Accuracy
QAT q4_0 Model:
Checks both TLS version and cipher suites, which are critical for assessing quantum resistance. While the implementation has flaws (e.g., assuming TLS version is exposed in HTTP headers), the approach aligns with security best practices.
Explicitly acknowledges limitations (e.g., "not a definitive test") and avoids overpromising.
BF16 Model:
Relies on checking for a non-standard TLS/1.3 header, which does not exist in HTTP responses. TLS version is part of the SSL/TLS handshake and cannot be retrieved via HttpClient headers.
Contains incorrect logic (e.g., client.GetAwaiter().GetResult(null) is nonsensical and throws runtime errors).
Code Quality
QAT q4_0 Model:
Uses modern async/await patterns for non-blocking I/O.
Separates concerns into methods (CheckTLSVersionAsync, CheckCipherSuiteAsync).
Includes robust error handling and logging.
BF16 Model:
Uses blocking synchronous code (GetAwaiter().GetResult()), which violates .NET best practices and risks deadlocks.
Poorly structured (e.g., redundant using blocks, unclear variable names like result).
Security Relevance
QAT q4_0 Model:
Focuses on cipher suites, which are closer to the actual indicators of quantum resistance (e.g., AES-256-GCM). While not truly quantum-safe, these are stronger than outdated algorithms.
Mentions the need to update cipher lists based on NIST guidelines.
BF16 Model:
Misleadingly claims to check for "AES-256-CBC" (a deprecated cipher mode) but never implements it.
Fails to address cipher suites entirely, rendering the check meaningless.
Realism
QAT q4_0 Model:
Acknowledges the complexity of quantum-safe detection and clarifies that HTTP-based checks are insufficient. This aligns with real-world security practices.
BF16 Model:
Implies that checking for TLS 1.3 guarantees quantum safety, which is false. TLS 1.3 uses classical cryptography and is not inherently quantum-resistant.
Usability
QAT q4_0 Model:
Provides clear console output (e.g., "No quantum-resistant cipher suites detected").
Includes a working Main method with an example URL.
BF16 Model:
Fails to compile due to syntax errors (e.g., client.GetAwaiter().GetResult(null) is invalid).
Lacks meaningful output (e.g., no details about why a site is deemed insecure).
Critical Flaws in Both Models
Header Misuse: Both models incorrectly assume TLS version and cipher suites are exposed in HTTP headers (e.g., Sec-Cipher). In reality, this data is part of the SSL/TLS handshake and requires low-level inspection (e.g., using SslStream or libraries like BouncyCastle).
Quantum-Safe Misunderstanding: Neither code checks for post-quantum algorithms (e.g., CRYSTALS-Kyber). Current TLS 1.3 cipher suites are not quantum-safe, so both models provide false positives.
Final Verdict
The QAT q4_0 model's code is superior because it:
Follows better coding practices (async/await, error handling).
Attempts a more relevant security analysis (TLS + cipher suites).
Explicitly acknowledges limitations.
However, both models fail to solve the original problem due to fundamental misunderstandings of TLS/SSL mechanics. For a production-grade solution, direct inspection of the TLS handshake (e.g., via SslStream) and support for post-quantum algorithms would be required.
Overall the perp difference was small (my test set was also small) and running the Deepseek test produced different results on subsequent runs . So I can not come to a definite conclusion. But I would say is worth investigating further.
[!Note]
This repository corresponds to the 12 instruction-tuned version of the Gemma 3 model in GGUF format using Quantization Aware Training (QAT).
The GGUF corresponds to Q4_0 quantization.
Thanks to QAT, the model is able to preserve similar quality as bfloat16 while significantly reducing the memory requirements
to load the model.
Summary description and brief definition of inputs and outputs.
Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
Gemma 3 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. Gemma 3 has a large, 128K context window, multilingual support in over
140 languages, and is available in more sizes than previous versions. Gemma 3
models are well-suited for a variety of text generation and image understanding
tasks, including question answering, summarization, and reasoning. Their
relatively small size makes it possible to deploy them in environments with
limited resources such as laptops, desktops or your own cloud infrastructure,
democratizing access to state of the art AI models and helping foster innovation
for everyone.
Inputs and outputs
Input:
Text string, such as a question, a prompt, or a document to be summarized
Images, normalized to 896 x 896 resolution and encoded to 256 tokens
each
Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
32K tokens for the 1B size
Output:
Generated text in response to the input, such as an answer to a
question, analysis of image content, or a summary of a document
Total output context of 8192 tokens
Usage
Below, there are some code snippets on how to get quickly started with running the model.
llama.cpp (text-only)
./llama-cli -hf google/gemma-3-27b-it-qat-q4_0-gguf -p "Write a poem about the Kraken."
Data used for model training and how the data was processed.
Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources. the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
1B with 2 trillion tokens. Here are the key components:
Web Documents: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages.
Code: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions.
Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
Images: A wide range of images enables the model to perform image
analysis and visual data extraction tasks.
The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.
Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
was applied at multiple stages in the data preparation process to ensure
the exclusion of harmful and illegal content.
Sensitive Data Filtering: As part of making Gemma pre-trained models
safe and reliable, automated techniques were used to filter out certain
personal information and other sensitive data from training sets.
Additional methods: Filtering based on content quality and safety in
line with our policies.
Implementation Information
Details about the model internals.
Hardware
Gemma was trained using Tensor Processing Unit (TPU) hardware (TPUv4p,
TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
computational power. TPUs, designed specifically for matrix operations common in
machine learning, offer several advantages in this domain:
Performance: TPUs are specifically designed to handle the massive
computations involved in training VLMs. They can speed up training
considerably compared to CPUs.
Memory: TPUs often come with large amounts of high-bandwidth memory,
allowing for the handling of large models and batch sizes during training.
This can lead to better model quality.
Scalability: TPU Pods (large clusters of TPUs) provide a scalable
solution for handling the growing complexity of large foundation models.
You can distribute training across multiple TPU devices for faster and more
efficient processing.
Cost-effectiveness: In many scenarios, TPUs can provide a more
cost-effective solution for training large models compared to CPU-based
infrastructure, especially when considering the time and resources saved
due to faster training.
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models. ML
Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like these ones.
Together, JAX and ML Pathways are used as described in the
paper about the Gemini family of models; "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
Intended Usage
Open vision-language models (VLMs) models have a wide range of applications
across various industries and domains. The following list of potential uses is
not comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
Content Creation and Communication
Text Generation: These models can be used to generate creative text
formats such as poems, scripts, code, marketing copy, and email drafts.
Chatbots and Conversational AI: Power conversational interfaces
for customer service, virtual assistants, or interactive applications.
Text Summarization: Generate concise summaries of a text corpus,
research papers, or reports.
Image Data Extraction: These models can be used to extract,
interpret, and summarize visual data for text communications.
Research and Education
Natural Language Processing (NLP) and VLM Research: These
models can serve as a foundation for researchers to experiment with VLM
and NLP techniques, develop algorithms, and contribute to the
advancement of the field.
Language Learning Tools: Support interactive language learning
experiences, aiding in grammar correction or providing writing practice.
Knowledge Exploration: Assist researchers in exploring large
bodies of text by generating summaries or answering questions about
specific topics.
Limitations
Training Data
The quality and diversity of the training data significantly
influence the model's capabilities. Biases or gaps in the training data
can lead to limitations in the model's responses.
The scope of the training dataset determines the subject areas
the model can handle effectively.
Context and Task Complexity
Models are better at tasks that can be framed with clear
prompts and instructions. Open-ended or highly complex tasks might be
challenging.
A model's performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).
Language Ambiguity and Nuance
Natural language is inherently complex. Models might struggle
to grasp subtle nuances, sarcasm, or figurative language.
Factual Accuracy
Models generate responses based on information they learned
from their training datasets, but they are not knowledge bases. They
may generate incorrect or outdated factual statements.
Common Sense
Models rely on statistical patterns in language. They might
lack the ability to apply common sense reasoning in certain situations.
Ethical Considerations and Risks
The development of vision-language models (VLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the following:
Bias and Fairness
VLMs trained on large-scale, real-world text and image data can
reflect socio-cultural biases embedded in the training material. These
models underwent careful scrutiny, input data pre-processing described
and posterior evaluations reported in this card.
Misinformation and Misuse
VLMs can be misused to generate text that is false, misleading,
or harmful.
This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
A responsibly developed open model offers the opportunity to
share innovation by making VLM technology accessible to developers and
researchers across the AI ecosystem.
Risks identified and mitigations:
Perpetuation of biases: It's encouraged to perform continuous
monitoring (using evaluation metrics, human review) and the exploration of
de-biasing techniques during model training, fine-tuning, and other use
cases.
Generation of harmful content: Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.
Misuse for malicious purposes: Technical limitations and developer
and end-user education can help mitigate against malicious applications of
VLMs. Educational resources and reporting mechanisms for users to flag
misuse are provided. Prohibited uses of Gemma models are outlined in the
Gemma Prohibited Use Policy.
Privacy violations: Models were trained on data filtered for removal
of certain personal information and other sensitive data. Developers are
encouraged to adhere to privacy regulations with privacy-preserving
techniques.
Benefits
At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
