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Model: twinkle-ai/gemma-3-4B-T1-it-GGUF Source: Original Platform
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---
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license: gemma
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language:
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- en
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- zh
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base_model: twinkle-ai/gemma-3-4B-T1-it
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library_name: transformers
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tags:
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- Taiwan
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- SLM
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- GGUF
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- agent
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datasets:
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- lianghsun/tw-reasoning-instruct
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- lianghsun/tw-contract-review-chat
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- minyichen/tw-instruct-R1-200k
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- minyichen/tw_mm_R1
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- minyichen/LongPaper_multitask_zh_tw_R1
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- nvidia/Nemotron-Instruction-Following-Chat-v1
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metrics:
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- accuracy
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model-index:
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- name: gemma-3-4B-T1-it
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results:
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- task:
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type: question-answering
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name: Single Choice Question
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dataset:
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name: tmmlu+
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type: ikala/tmmluplus
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config: all
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split: test
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c
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metrics:
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- type: accuracy
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value: 47.44
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name: single choice
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- task:
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type: question-answering
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name: Single Choice Question
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dataset:
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name: mmlu
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type: cais/mmlu
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config: all
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split: test
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revision: c30699e
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metrics:
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- type: accuracy
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value: 59.13
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name: single choice
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- task:
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type: question-answering
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name: Single Choice Question
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dataset:
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name: tw-legal-benchmark-v1
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type: lianghsun/tw-legal-benchmark-v1
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config: all
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split: test
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revision: 66c3a5f
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metrics:
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- type: accuracy
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value: 44.18
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name: single choice
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pipeline_tag: text-generation
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---
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# Gemma 3 4B T1-it GGUF Collection
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<div align="center" style="line-height: 1;">
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<a href="https://discord.gg/Cx737yw4ed" target="_blank" style="margin: 2px;">
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<img alt="Discord" src="https://img.shields.io/badge/Discord-Twinkle%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
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</a>
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<a href="https://huggingface.co/twinkle-ai" target="_blank" style="margin: 2px;">
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<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Twinkle%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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</a>
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<!-- Gemma 模型在 Hugging Face 上為 gated，使用者需同意 Google usage license -->
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<a href="https://huggingface.co/google/gemma-3-4b-pt" style="margin: 2px;">
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<img alt="License" src="https://img.shields.io/badge/License-gemma-f5de53?&color=0081fb" style="display: inline-block; vertical-align: middle;"/>
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</a>
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</div>
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GGUF quantized models converted from [twinkle-ai/gemma-3-4B-T1-it](https://huggingface.co/twinkle-ai/gemma-3-4B-T1-it) for use with llama.cpp.
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## About
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Gemma 3 4B T1-it is a small language model fine-tuned on Taiwan-focused datasets, supporting both English and Traditional Chinese. This repository provides multiple quantization formats optimized for different use cases.
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## Available Models
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| Model | Size | Use Case |
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| ----- | ---- | -------- |
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| `twinkle-ai-gemma-3-4B-T1-it-BF16.gguf` | Largest | Best quality, highest precision |
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| `twinkle-ai-gemma-3-4B-T1-it-F16.gguf` | Large | High quality, good precision |
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| `twinkle-ai-gemma-3-4B-T1-it-Q8_0.gguf` | Medium | Balanced quality and speed |
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| `twinkle-ai-gemma-3-4b-t1-it-q4_k_m.gguf` | Smallest | Fastest inference, lower memory |
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## Quick Start
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### Option 1: Using Hugging Face Hub (Recommended)
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Install llama.cpp via Homebrew:
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```bash
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brew install llama.cpp
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```
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Run inference directly from Hugging Face:
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```bash
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llama-cli --hf-repo thliang01/gemma-3-4B-T1-it-Q8_0-GGUF \
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--hf-file gemma-3-4b-t1-it-q8_0.gguf \
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-p "Your prompt here"
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```
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Start as a server:
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```bash
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llama-server --hf-repo thliang01/gemma-3-4B-T1-it-Q8_0-GGUF \
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--hf-file gemma-3-4b-t1-it-q8_0.gguf \
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-c 2048
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```
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### Option 2: Build from Source
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#### Step 1: Clone llama.cpp repository
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```bash
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git clone https://github.com/ggerganov/llama.cpp
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cd llama.cpp
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```
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#### Step 2: Build llama.cpp
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Basic build (CPU only):
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```bash
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LLAMA_CURL=1 make
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```
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**Hardware-specific build options:**
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- **NVIDIA GPU (Linux)**:
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```bash
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LLAMA_CUDA=1 LLAMA_CURL=1 make
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```
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- **Apple Silicon (Mac)**:
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```bash
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LLAMA_METAL=1 LLAMA_CURL=1 make
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```
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- **AMD GPU (ROCm)**:
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```bash
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LLAMA_HIPBLAS=1 LLAMA_CURL=1 make
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```
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#### Step 3: Run inference
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```bash
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./llama-cli --hf-repo thliang01/gemma-3-4B-T1-it-Q8_0-GGUF \
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--hf-file gemma-3-4b-t1-it-q8_0.gguf \
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-p "Your prompt here"
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```
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#### Step 4: Start server (optional)
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```bash
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./llama-server --hf-repo thliang01/gemma-3-4B-T1-it-Q8_0-GGUF \
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--hf-file gemma-3-4b-t1-it-q8_0.gguf \
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-c 2048
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```
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## Advanced Usage
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### Choosing the Right Model
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Select a model based on your needs:
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- **Best Quality**: Use `BF16` or `F16` versions (requires more memory)
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- **Balanced**: Use `Q8_0` version (recommended for most users)
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- **Resource Constrained**: Use `q4_k_m` version (suitable for devices with limited memory)
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### Common Parameters
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- `-p "prompt"`: Your input text for the model to respond to
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- `-c 2048`: Context length (maximum number of tokens that can be processed)
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- `--hf-repo`: Hugging Face repository name
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- `--hf-file`: Model file name to use
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### Adjusting Generation Parameters
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```bash
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llama-cli --hf-repo thliang01/gemma-3-4B-T1-it-Q8_0-GGUF \
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--hf-file gemma-3-4b-t1-it-q8_0.gguf \
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-p "Your prompt here" \
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--temp 0.7 \
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--top-p 0.9 \
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--repeat-penalty 1.1
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```
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Parameter explanations:
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- `--temp`: Temperature (0.0-2.0), higher values produce more random output
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- `--top-p`: Nucleus sampling parameter (0.0-1.0)
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- `--repeat-penalty`: Repetition penalty to avoid repetitive content
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## Model Information
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- **Base Model**: twinkle-ai/gemma-3-4B-T1-it
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- **Languages**: English, Traditional Chinese
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- **License**: Gemma
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- **Format**: GGUF (converted via [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo))
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### Training Data
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- Taiwan reasoning and instruction datasets
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- Contract review and legal documents
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- Multimodal and long-form content
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- Instruction-following examples
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### Benchmarks
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- **TMMLU+**: 47.44% accuracy
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- **MMLU**: 59.13% accuracy
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- **TW Legal Benchmark**: 44.18% accuracy
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## Troubleshooting
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### Common Issues
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**Q: Getting out of memory errors?**
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A: Try using a smaller quantized version like `q4_k_m`, or reduce the context length parameter `-c`.
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**Q: How can I speed up inference?**
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A:
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1. Use GPU acceleration (add hardware-specific flags during compilation)
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2. Choose a smaller quantized model (like `q4_k_m`)
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3. Reduce context length
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**Q: What prompt format does the model support?**
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A: This is an instruction-tuned model. Use a clear instruction format, for example:
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```text
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Please analyze the main clauses of the following contract: [contract content]
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```
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## Links
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- [Original Model](https://huggingface.co/twinkle-ai/gemma-3-4B-T1-it)
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- [llama.cpp Documentation](https://github.com/ggerganov/llama.cpp)
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- [GGUF Format Documentation](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md)
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## Contributing
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If you have any questions or suggestions, please feel free to open a discussion in the Hugging Face repository.
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---
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**Note**: On first run, llama.cpp will automatically download the model file from Hugging Face. Please ensure you have a stable internet connection.
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gemma-3-4b-t1-it-q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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size 4843612288
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version https://git-lfs.github.com/spec/v1
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oid sha256:603a24fd155cf7305c01f684ada5c55135fc65dbd65ded2a4951a24abff1fac7
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size 9110313152
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version https://git-lfs.github.com/spec/v1
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oid sha256:1d1a1e347a0067d4a35993017c54cf6874ab7dcff9363bef6d64d4440793cd90
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size 9110313152
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version https://git-lfs.github.com/spec/v1
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oid sha256:eb92173a84b5a97fed84bc35b7ea6fdeba2ae842904d6a1936447c14061be386
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size 4843612352
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version https://git-lfs.github.com/spec/v1
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oid sha256:56e173514961512231dfd86099af8f53bb6a4a12663595deb8b1eb4363f4e5d1
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size 2867477888
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