--- license: gemma pipeline_tag: text-generation tags: - gemma --- # Gemma-3 1B Instruct GGUF Models **Note llama-quantize was not able to fully quantize the ggufs for k quants as the tensor dimensions of some weights where not divisible by 256. fallback quants where used.** ## **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. πŸ“Œ **Use BF16 if:** βœ” Your hardware has native **BF16 support** (e.g., newer GPUs, TPUs). βœ” You want **higher precision** while saving memory. βœ” You plan to **requantize** the model into another format. πŸ“Œ **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. πŸ“Œ **Use F16 if:** βœ” Your hardware supports **FP16** but **not BF16**. βœ” You need a **balance between speed, memory usage, and accuracy**. βœ” You are running on a **GPU** or another device optimized for FP16 computations. πŸ“Œ **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)** β†’ **Best for minimal memory usage**, may have lower precision. - **Higher-bit models (Q6_K, Q8_0)** β†’ **Better accuracy**, requires more memory. πŸ“Œ **Use Quantized Models if:** βœ” You are running inference on a **CPU** and need an optimized model. βœ” Your device has **low VRAM** and cannot load full-precision models. βœ” You want to reduce **memory footprint** while keeping reasonable accuracy. πŸ“Œ **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-3-1b-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-3-1b-it-f16.gguf` - Model weights stored in **F16**. - Use if your device supports **FP16**, especially if BF16 is not available. ### `google_gemma-3-1b-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-3-1b-it-f16-q8.gguf` - **Output & embeddings** remain in **F16**. - All other layers quantized to **Q8_0**. ### `google_gemma-3-1b-it-q4_k.gguf` - **Output & embeddings** quantized to **Q8_0**. - All other layers quantized to **Q4_K**. - Good for **CPU inference** with limited memory. ### `google_gemma-3-1b-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-3-1b-it-q6_k.gguf` - **Output & embeddings** quantized to **Q8_0**. - All other layers quantized to **Q6_K** . ### `google_gemma-3-1b-it-q8.gguf` - Fully **Q8** quantized model for better accuracy. - Requires **more memory** but offers higher precision. # Gemma 3 model card **Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core) **Resources and Technical Documentation**: * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] **Terms of Use**: [Terms][terms] **Authors**: Google DeepMind ## Model Information 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 1B model handles text only. ### Inputs and outputs - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Total input context 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 # πŸš€ If you find these models useful Please give like a click ❀️ . Also I’d really appreciate it if you could test my Network Monitor Assistant at πŸ‘‰ [Network Monitor Assitant](https://readyforquantum.com). πŸ’¬ 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 timeβ€”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](https://readyforquantum.com) or [Download](https://readyforquantum.com/download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) the Quantum Network Monitor agent to get more tokens, Alternatively use the TestLLM . πŸ”΅ **HugLLM** – Runs **open-source Hugging Face models** Fast, Runs small models (β‰ˆ8B) hence lower quality, Get 2x more tokens (subject to Hugging Face API availability)