gpt2 GGUF Models
Model Generation Details
This model was generated using
llama.cpp at commit
5787b5da.
Quantization beyond the IMatrix
Testing a new quantization method using rules to bump important layers above what the standard imatrix would use.
I have found that the standard IMatrix does not perform very well at low bit quantiztion and for MOE models. So I am using llama.cpp --tensor-type to bump up selected layers. See
Layer bumping with llama.cppThis does create larger model files but increases precision for a given model size.
Please provide feedback on how you find this method performs
Choosing the Right Model Format
Selecting the correct model format depends on your hardware capabilities and memory constraints.
BF16 (Brain Float 16) – Use if BF16 acceleration is available
- A 16-bit floating-point format designed for faster computation while retaining good precision.
- Provides similar dynamic range as FP32 but with lower memory usage.
- Recommended if your hardware supports BF16 acceleration (check your device's specs).
- Ideal for high-performance inference with reduced memory footprint compared to FP32.
📌 Use BF16 if:
✔ Your hardware has native BF16 support (e.g., newer GPUs, TPUs).
✔ You want higher precision while saving memory.
✔ You plan to requantize the model into another format.
📌 Avoid BF16 if:
❌ Your hardware does not support BF16 (it may fall back to FP32 and run slower).
❌ You need compatibility with older devices that lack BF16 optimization.
F16 (Float 16) – More widely supported than BF16
- A 16-bit floating-point high precision but with less of range of values than BF16.
- Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
- Slightly lower numerical precision than BF16 but generally sufficient for inference.
📌 Use F16 if:
✔ Your hardware supports FP16 but not BF16.
✔ You need a balance between speed, memory usage, and accuracy.
✔ You are running on a GPU or another device optimized for FP16 computations.
📌 Avoid F16 if:
❌ Your device lacks native FP16 support (it may run slower than expected).
❌ You have memory limitations.
Hybrid Precision Models (e.g., bf16_q8_0, f16_q4_K) – Best of Both Worlds
These formats selectively quantize non-essential layers while keeping key layers in full precision (e.g., attention and output layers).
- Named like
bf16_q8_0 (meaning full-precision BF16 core layers + quantized Q8_0 other layers).
- Strike a balance between memory efficiency and accuracy, improving over fully quantized models without requiring the full memory of BF16/F16.
📌 Use Hybrid Models if:
✔ You need better accuracy than quant-only models but can’t afford full BF16/F16 everywhere.
✔ Your device supports mixed-precision inference.
✔ You want to optimize trade-offs for production-grade models on constrained hardware.
📌 Avoid Hybrid Models if:
❌ Your target device doesn’t support mixed or full-precision acceleration.
❌ You are operating under ultra-strict memory limits (in which case use fully quantized formats).
Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.
- Lower-bit models (Q4_K) → Best for minimal memory usage, may have lower precision.
- Higher-bit models (Q6_K, Q8_0) → Better accuracy, requires more memory.
📌 Use Quantized Models if:
✔ You are running inference on a CPU and need an optimized model.
✔ Your device has low VRAM and cannot load full-precision models.
✔ You want to reduce memory footprint while keeping reasonable accuracy.
📌 Avoid Quantized Models if:
❌ You need maximum accuracy (full-precision models are better for this).
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).
Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)
These models are optimized for very high memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.
-
IQ3_XS: Ultra-low-bit quantization (3-bit) with very high memory efficiency.
- Use case: Best for ultra-low-memory devices where even Q4_K is too large.
- Trade-off: Lower accuracy compared to higher-bit quantizations.
-
IQ3_S: Small block size for maximum memory efficiency.
- Use case: Best for low-memory devices where IQ3_XS is too aggressive.
-
IQ3_M: Medium block size for better accuracy than IQ3_S.
- Use case: Suitable for low-memory devices where IQ3_S is too limiting.
-
Q4_K: 4-bit quantization with block-wise optimization for better accuracy.
- Use case: Best for low-memory devices where Q6_K is too large.
-
Q4_0: Pure 4-bit quantization, optimized for ARM devices.
- Use case: Best for ARM-based devices or low-memory environments.
Ultra Low-Bit Quantization (IQ1_S IQ1_M IQ2_S IQ2_M IQ2_XS IQ2_XSS)
- *Ultra-low-bit quantization (1 2-bit) with extreme memory efficiency.
- Use case: Best for cases were you have to fit the model into very constrained memory
- Trade-off: Very Low Accuracy. May not function as expected. Please test fully before using.
Summary Table: Model Format Selection
| Model Format | Precision | Memory Usage | Device Requirements | Best Use Case |
|---|
| BF16 | Very High | High | BF16-supported GPU/CPU | High-speed inference with reduced memory |
| F16 | High | High | FP16-supported GPU/CPU | Inference when BF16 isn’t available |
| Q4_K | Medium-Low | Low | CPU or Low-VRAM devices | Memory-constrained inference |
| Q6_K | Medium | Moderate | CPU with more memory | Better accuracy with quantization |
| Q8_0 | High | Moderate | GPU/CPU with moderate VRAM | Highest accuracy among quantized models |
| IQ3_XS | Low | Very Low | Ultra-low-memory devices | Max memory efficiency, low accuracy |
| IQ3_S | Low | Very Low | Low-memory devices | Slightly more usable than IQ3_XS |
| IQ3_M | Low-Medium | Low | Low-memory devices | Better accuracy than IQ3_S |
| Q4_0 | Low | Low | ARM-based/embedded devices | Llama.cpp automatically optimizes for ARM inference |
| Ultra Low-Bit (IQ1/2_*) | Very Low | Extremely Low | Tiny edge/embedded devices | Fit models in extremely tight memory; low accuracy |
Hybrid (e.g., bf16_q8_0) | Medium–High | Medium | Mixed-precision capable hardware | Balanced performance and memory, near-FP accuracy in critical layers |
GPT-2
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at
this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
Model description
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token i only uses the inputs from 1 to i but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
This is the smallest version of GPT-2, with 124M parameters.
Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
model hub to look for fine-tuned versions on a task that interests you.
How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
1>>> from transformers import pipeline, set_seed
2>>> generator = pipeline('text-generation', model='gpt2')
3>>> set_seed(42)
4>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
5
6[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
7 {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
8 {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
9 {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
10 {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
Here is how to use this model to get the features of a given text in PyTorch:
1from transformers import GPT2Tokenizer, GPT2Model
2tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
3model = GPT2Model.from_pretrained('gpt2')
4text = "Replace me by any text you'd like."
5encoded_input = tokenizer(text, return_tensors='pt')
6output = model(**encoded_input)
and in TensorFlow:
1from transformers import GPT2Tokenizer, TFGPT2Model
2tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
3model = TFGPT2Model.from_pretrained('gpt2')
4text = "Replace me by any text you'd like."
5encoded_input = tokenizer(text, return_tensors='tf')
6output = model(encoded_input)
Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
model card:
Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
that require the generated text to be true.
Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
levels of caution around use cases that are sensitive to biases around human attributes.
Here's an example of how the model can have biased predictions:
1>>> from transformers import pipeline, set_seed
2>>> generator = pipeline('text-generation', model='gpt2')
3>>> set_seed(42)
4>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
5
6[{'generated_text': 'The White man worked as a mannequin for'},
7 {'generated_text': 'The White man worked as a maniser of the'},
8 {'generated_text': 'The White man worked as a bus conductor by day'},
9 {'generated_text': 'The White man worked as a plumber at the'},
10 {'generated_text': 'The White man worked as a journalist. He had'}]
11
12>>> set_seed(42)
13>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
14
15[{'generated_text': 'The Black man worked as a man at a restaurant'},
16 {'generated_text': 'The Black man worked as a car salesman in a'},
17 {'generated_text': 'The Black man worked as a police sergeant at the'},
18 {'generated_text': 'The Black man worked as a man-eating monster'},
19 {'generated_text': 'The Black man worked as a slave, and was'}]
This bias will also affect all fine-tuned versions of this model.
Training data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
here.
Training procedure
Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|---|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 |
BibTeX entry and citation info
1@article{radford2019language,
2 title={Language Models are Unsupervised Multitask Learners},
3 author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
4 year={2019}
5}
🚀 If you find these models useful
Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:
The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) :
Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself
GGUFModelBuilder💬 How to test:
Choose an AI assistant type:
TurboLLM (GPT-4.1-mini)HugLLM (Hugginface Open-source models)TestLLM (Experimental CPU-only)
What I’m Testing
I’m pushing the limits of small open-source models for AI network monitoring, specifically:
- Function calling against live network services
- How small can a model go while still handling:
- Automated Nmap security scans
- Quantum-readiness checks
- Network Monitoring tasks
🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):
- ✅ Zero-configuration setup
- ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
- 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!
Other Assistants
🟢 TurboLLM – Uses gpt-4.1-mini :
- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
- Create custom cmd processors to run .net code on Quantum Network Monitor Agents
- Real-time network diagnostics and monitoring
- Security Audits
- Penetration testing (Nmap/Metasploit)
🔵 HugLLM – Latest Open-source models:
- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.
💡 Example commands you could test:
"Give me info on my websites SSL certificate""Check if my server is using quantum safe encyption for communication""Run a comprehensive security audit on my server"- '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!
Final Word
I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is
open source. Feel free to use whatever you find helpful.
If you appreciate the work, please consider
buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.
I'm also open to job opportunities or sponsorship.
Thank you! 😊
