I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.
In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp
While this does increase model file size, it significantly improves precision for a given quantization level.
I'd love your feedback—have you tried this? How does it perform for you?
Yes, this is official, and yes, this is, to my knowledge, a real version of Llama 3.3 8B. (I think, anyways)
Facebook has a Llama API available that allows for inference of the other Llama models (L3.3 70B, L4 Scout and Maverick), but also includes a special, new (according to the original press release) "Llama 3.3 8B" that didn't exist anywhere else and was stuck behind the Facebook API!
However. The Llama API supports finetuning L3.3... and downloading the final model in HF format. Problem solved, right?
Wellllllllllllllll. Not really. The finetuning API was hidden behind layers of support tickets. I tried when the original API dropped in April, and was just told "We'll think about it and send you any updates" (there never were any updates).
Flash forward to December, on a whim I decide to look at the API again. And... by god... the finetuning tab was there. I could click on it and start a job (please ignore that I have no idea how it works, and in fact the finetuning tab actually disappeared after the first time I clicked on it, though I could still manually go to the page).
Apparently, this was not very well tested, as there were a good few bugs, the UI was janky, and the download model function did not actually work due to CORS (I had to manually curl things to get the CDN link).
But... by god... the zip file downloaded, and I had my slightly finetuned model.
To my shock and delight, however, they also provide the adapter that they merged into the model. That means I can subtract that adapter and get the original model. And... here we are!
Benchmarks
Llama 3.1 8B Instruct
Llama 3.3 8B Instruct (maybe)
IFEval (1 epoch, score avged across all strict/loose instruction/prompt accuracies to follow Llama 3 paper)
78.2
81.95
GPQA Diamond (3 epochs)
29.3
37.0
All benchmarks done in OpenBench at 1.0 temp.
More cursed discoveries
Apparently the context length of the original Llama 3.3 model (i.e. the regular one that the Llama API serves) is 128k, while the finetunable version is only 8k. This is true across both the downloaded version and the version of the finetune served by the API (refer to screenshot with 10k tokens worth of a as input). This does not really make any coherent sense.
image
Are you sure this is really Llama 3.3?
As far as I'm aware! It has stylistic tics that differ from Llama 3 and 3.1.
There are a few weird artifacts, however, of the base model being the... original Llama 3(???)
The original ZIP included an original_repo_id.json that contained:
and, furthermore, the adapter_config.jsonalso had Llama 3 as the base model. However, the models clearly act different and know different things! Furthermore, I tested across the Llama API and my copy, and they both share the same differences from L3 and L3.1.
Suffice it to say, I'm pretty sure this is really Llama 3.3 8B.
For example, via the Llama API, you may receive access to the Llama 3.3 8b model, which is considered a Llama AI model and part of the Meta AI Materials; when downloaded, and not accessed via the Llama API, the Llama 3.3 8b model is subject to the Llama 3.3 Community License Agreement and Acceptable Use Policy.
The Llama 3.3 8b model (after downloading) is subject to the regular L3.3 license, which allows for redistribution. So... as far as I can tell, yes, this is perfectly legal to redistribute!
Please email me at fizzarolli [at] riseup.net for any conerns. If Meta would like me to take this model down, please have someone email me and ask from an official Meta address.
🚀 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 on. 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.
