Running in chat mode, available commands:
/image <path> load an image
/clear clear the chat history
/quit or /exit exit the program
> /image car-1.jpg
Encoding image car-1.jpg
Image encoded in 46305 ms
Image decoded in 19302 ms
> what is the image of
Here's a breakdown of what's in the image:
**Subject:** The primary subject is a black Porsche Panamera Turbo driving on a highway.
**Details:**
* **Car:** It's a sleek, modern Porsche Panamera Turbo, identifiable by its distinctive rear design, the "PORSCHE" lettering, and the "Panamera Turbo" badge. The license plate reads "CVC-911".
* **Setting:** The car is on a multi-lane highway, with a blurred background of trees, a distant building, and a cloudy sky. The lighting suggests it's either dusk or dawn.
* **Motion:** The image captures the car in motion, with a slight motion blur to convey speed.
**Overall Impression:** The image conveys a sense of speed, luxury, and power. It's a well-composed shot that highlights the car's design and performance.
Do you want me to describe any specific aspect of the image in more detail, or perhaps analyze its composition?
🚀 If you find these models useful
Please click like ❤️ . Also I’d really appreciate it if you could test my Network Monitor Assistant at 👉 Network Monitor Assitant.
💬 Click the chat icon (bottom right of the main and dashboard pages) . Choose a LLM; toggle between the LLM Types TurboLLM -> FreeLLM -> TestLLM.
What I'm Testing
I'm experimenting with function calling against my network monitoring service. Using small open source models. I am into the question "How small can it go and still function".
🟡 TestLLM – Runs Phi-4-mini-instruct using phi-4-mini-q4_0.gguf , llama.cpp on 6 threads of a Cpu VM (Should take about 15s to load. Inference speed is quite slow and it only processes one user prompt at a time—still working on scaling!). If you're curious, I'd be happy to share how it works! .
The other Available AI Assistants
🟢 TurboLLM – Uses gpt-4o-mini Fast! . Note: tokens are limited since OpenAI models are pricey, but you can Login or Download the Quantum Network Monitor agent to get more tokens, Alternatively use the TestLLM .
🔵 HugLLM – Runs open-source Hugging Face models Fast, Runs small models (≈8B) hence lower quality, Get 2x more tokens (subject to Hugging Face API availability)
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 limtations.
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).
Summary Table: Model Format Selection
Model Format
Precision
Memory Usage
Device Requirements
Best Use Case
BF16
Highest
High
BF16-supported GPU/CPUs
High-speed inference with reduced memory
F16
High
High
FP16-supported devices
GPU inference when BF16 isn’t available
Q4_K
Low
Very Low
CPU or Low-VRAM devices
Best for memory-constrained environments
Q6_K
Medium Low
Low
CPU with more memory
Better accuracy while still being quantized
Q8
Medium
Moderate
CPU or GPU with enough VRAM
Best accuracy among quantized models
Included Files & Details
google_gemma-3-12b-it-bf16.gguf
Model weights preserved in BF16.
Use this if you want to requantize the model into a different format.
Best if your device supports BF16 acceleration.
google_gemma-3-12b-it-f16.gguf
Model weights stored in F16.
Use if your device supports FP16, especially if BF16 is not available.
google_gemma-3-12b-it-bf16-q8.gguf
Output & embeddings remain in BF16.
All other layers quantized to Q8_0.
Use if your device supports BF16 and you want a quantized version.
google_gemma-3-12b-it-f16-q8.gguf
Output & embeddings remain in F16.
All other layers quantized to Q8_0.
google_gemma-3-12b-it-q4_k_l.gguf
Output & embeddings quantized to Q8_0.
All other layers quantized to Q4_K.
Good for CPU inference with limited memory.
google_gemma-3-12b-it-q4_k_m.gguf
Similar to Q4_K.
Another option for low-memory CPU inference.
google_gemma-3-12b-it-q4_k_s.gguf
Smallest Q4_K variant, using less memory at the cost of accuracy.
Best for very low-memory setups.
google_gemma-3-12b-it-q6_k_l.gguf
Output & embeddings quantized to Q8_0.
All other layers quantized to Q6_K .
google_gemma-3-12b-it-q6_k_m.gguf
A mid-range Q6_K quantized model for balanced performance .
Suitable for CPU-based inference with moderate memory.
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
