ggml-org

Various models to be used in llama.cpp CI workflow.

Detailed guide for using this model with `llama.cpp`:

Original model: https://huggingface.co/HuggingFaceTB/SmolVLM-500M-Instruct For more info, please refer to this PR: https://github.com/ggml-org/llama.cpp/pull/13050

This model is ModelCloud/tinyllama-15M-stories repeated 4 times to make 4 experts. The model is used for testing, not intended to be used in production (unless your product is some kind of bedtime story teller) A LoRA adapter trained on first 100 paragraphs of shakespeare can be found inside `moeshakespeare15M` With input: `Look in thy glass` - Original model generates: `Look in thy glass was a little girl. She was only three years old and she was three years old. She was` - LoRA adapter generates: `Look in thy glass in love of the eye: That's when when the eye see thy on the sun'`

Original model: https://huggingface.co/Qwen/Qwen3-0.6B

Original model: https://huggingface.co/ibm-granite/granite-docling-258M Related PR: https://github.com/ggml-org/llama.cpp/pull/16206

ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q80-GGUF This model was converted to GGUF format from `Qwen/Qwen3-Coder-30B-A3B-Instruct` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

Detailed guide for using this model with `llama.cpp`:

Original model: https://huggingface.co/mistral-community/pixtral-12b For more info, please refer to this PR: https://github.com/ggml-org/llama.cpp/pull/13065

ggml-org/Meta-Llama-3.1-8B-Instruct-Q40-GGUF This model was converted to GGUF format from `meta-llama/Meta-Llama-3.1-8B-Instruct` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

Related to this PR: https://github.com/ggml-org/llama.cpp/pull/13282 Quantizations for text model are taken from unsloth, all credits to them!

Original model: https://huggingface.co/HuggingFaceTB/SmolLM3-3B > [!IMPORTANT] > To enable thinking, you need to specify `--jinja` 1. Model Summary 2. Evaluation 3. Training 4. Limitations 5. License SmolLM3 is a 3B parameter language model designed to push the boundaries of small models. It supports 6 languages, advanced reasoning and long context. SmolLM3 is a fully open model that offers strong performance at the 3B–4B scale. The model is a decoder-only transformer using GQA and NoRope, it was pretrained on 11.2T tokens with a staged curriculum of web, code, math and reasoning data. Post-training included midtraining on 140B reasoning tokens followed by supervised fine-tuning and alignment via Anchored Preference Optimization (APO). Key features - Instruct model optimized for hybrid reasoning - Fully open model: open weights + full training details including public data mixture and training configs - Long context: Trained on 64k context and suppots up to 128k tokens using YARN extrapolation - Multilingual: 6 natively supported (English, French, Spanish, German, Italian, and Portuguese) How to use The modeling code for SmolLM3 is available in transformers `v4.53.0`, so make sure to upgrade your transformers version. You can also load the model with the latest `vllm` which uses transformers as a backend. For local inference, you can use `llama.cpp`, `ONNX`, `MLX` and `MLC`. You can find quantized checkpoints in this collection [TODO]. In this section, we report the evaluation results of SmolLM3 base model. All evaluations are zero-shot unless stated otherwise, and we use lighteval to run them. For Ruler 64k evaluation, we apply YaRN to the Qwen models with 32k context to extrapolate the context length. We highlight the best score in bold and underline the second-best score. English benchmarks Note: All evaluations are zero-shot unless stated otherwise. | Category | Metric | SmolLM3-3B | Qwen2.5-3B | Llama3-3.2B | Qwen3-1.7B-Base | Qwen3-4B-Base | |---------|--------|---------------------|------------|--------------|------------------|---------------| | Reasoning & Commonsense| HellaSwag | 76.15 | 74.19 | 75.52 | 60.52 | 74.37 | | | ARC-CF (Average) | 65.61 | 59.81 | 58.58 | 55.88 | 62.11 | | | Winogrande | 58.88 | 61.41 | 58.72 | 57.06 | 59.59 | | | CommonsenseQA | 55.28 | 49.14 | 60.60 | 48.98 | 52.99 | | Knowledge & Understanding | MMLU-CF (Average) | 44.13 | 42.93 | 41.32 | 39.11 | 47.65 | | | MMLU Pro CF | 19.61 | 16.66 | 16.42 | 18.04 | 24.92 | | | MMLU Pro MCF | 32.70 | 31.32 | 25.07 | 30.39 | 41.07 | | | PIQA | 78.89 | 78.35 | 78.51 | 75.35 | 77.58 | | | OpenBookQA | 40.60 | 40.20 | 42.00 | 36.40 | 42.40 | | | BoolQ | 78.99 | 73.61 | 75.33 | 74.46 | 74.28 | | Math & Code | | | | | | | | Coding & math | HumanEval+ | 30.48 | 34.14| 25.00 | 43.29 | 54.87 | | | MBPP+ | 52.91 | 52.11 | 38.88| 59.25 | 63.75 | | | MATH (4-shot) | 46.10 | 40.10 | 7.44 | 41.64 | 51.20 | | | GSM8k (5-shot) | 67.63 | 70.13 | 25.92 | 65.88 | 74.14 | | Long context | | | | | | | | | Ruler 32k context | 76.35 | 75.93 | 77.58 | 70.63 | 83.98 | | | Ruler 64k context | 67.85 | 64.90 | 72.93 | 57.18 | 60.29 | | Category | Metric | SmolLM3 3B Base | Qwen2.5-3B | Llama3.2 3B | Qwen3 1.7B Base | Qwen3 4B Base | |---------|--------|---------------------|------------|--------------|------------------|---------------| | Main supported languages | | | | | | | | | French| MLMM Hellaswag | 63.94 | 57.47 | 57.66 | 51.26 | 61.00 | | | Belebele | 51.00 | 51.55 | 49.22 |49.44| 55.00 | | | Global MMLU (CF) | 38.37 | 34.22 | 33.71 | 34.94 |41.80 | | | Flores-200 (5-shot) | 62.85| 61.38| 62.89 | 58.68 | 65.76 | | Spanish| MLMM Hellaswag | 65.85 | 58.25 | 59.39 | 52.40 | 61.85 | | | Belebele | 47.00 | 48.88 | 47.00 | 47.56 | 50.33 | | | Global MMLU (CF) | 38.51 | 35.84 | 35.60 | 34.79 |41.22 | | | Flores-200 (5-shot) | 48.25 | 50.00| 44.45 | 46.93 | 50.16 | | German| MLMM Hellaswag | 59.56 | 49.99| 53.19|46.10| 56.43 | | | Belebele | 48.44 | 47.88 | 46.22 | 48.00 | 53.44| | | Global MMLU (CF) | 35.10 | 33.19 | 32.60 | 32.73 |38.70 | | | Flores-200 (5-shot) | 56.60| 50.63| 54.95 | 52.58 | 50.48 | | Italian| MLMM Hellaswag | 62.49 | 53.21 | 54.96 | 48.72 | 58.76 | | | Belebele | 46.44 | 44.77 | 43.88 | 44.00 | 48.78 | 44.88 | | | Global MMLU (CF) | 36.99 | 33.91 | 32.79 | 35.37 |39.26 | | | Flores-200 (5-shot) | 52.65 | 54.87| 48.83 | 48.37 | 49.11 | | Portuguese| MLMM Hellaswag | 63.22 | 57.38 | 56.84 | 50.73 | 59.89 | | | Belebele | 47.67 | 49.22 | 45.00 | 44.00 | 50.00 | 49.00 | | | Global MMLU (CF) | 36.88 | 34.72 | 33.05 | 35.26 |40.66 | | | Flores-200 (5-shot) | 60.93 |57.68| 54.28 | 56.58 | 63.43 | The model has also been trained on Arabic (standard), Chinese and Russian data, but has seen fewer tokens in these languages compared to the 6 above. We report the performance on these langages for information. | Category | Metric | SmolLM3 3B Base | Qwen2.5-3B | Llama3.2 3B | Qwen3 1.7B Base | Qwen3 4B Base | |---------|--------|---------------------|------------|--------------|------------------|---------------| | Other supported languages | | | | | | | | | Arabic| Belebele | 40.22 | 44.22 | 45.33 | 42.33 | 51.78 | | | Global MMLU (CF) | 28.57 | 28.81 | 27.67 | 29.37 | 31.85 | | | Flores-200 (5-shot) | 40.22 | 39.44 | 44.43 | 35.82 | 39.76 | | Chinese| Belebele | 43.78 | 44.56 | 49.56 | 48.78 | 53.22 | | | Global MMLU (CF) | 36.16 | 33.79 | 39.57 | 38.56 | 44.55 | | | Flores-200 (5-shot) | 29.17 | 33.21 | 31.89 | 25.70 | 32.50 | | Russian| Belebele | 47.44 | 45.89 | 47.44 | 45.22 | 51.44 | | | Global MMLU (CF) | 36.51 | 32.47 | 34.52 | 34.83 | 38.80 | | | Flores-200 (5-shot) | 47.13 | 48.74 | 50.74 | 54.70 | 60.53 | No Extended Thinking Evaluation results of non reasoning models and reasoning models in no thinking mode. We highlight the best and second-best scores in bold. | Category | Metric | SmoLLM3-3B | Qwen2.5-3B | Llama3.1-3B | Qwen3-1.7B | Qwen3-4B | |---------|--------|------------|------------|-------------|------------|----------| | High school math competition | AIME 2025 | 9.3 | 2.9 | 0.3 | 8.0 | 17.1 | | Math problem-solving | GSM-Plus | 72.8 | 74.1 | 59.2 | 68.3 | 82.1 | | Competitive programming | LiveCodeBench v4 | 15.2 | 10.5 | 3.4 | 15.0 | 24.9 | | Graduate-level reasoning | GPQA Diamond | 35.7 | 32.2 | 29.4 | 31.8 | 44.4 | | Instruction following | IFEval | 76.7 | 65.6 | 71.6 | 74.0 | 68.9 | | Alignment | MixEval Hard | 26.9 | 27.6 | 24.9 | 24.3 | 31.6 | | Knowledge | MMLU-Pro | 45.0 | 41.9 | 36.6 | 45.6 | 60.9 | | Multilingual Q&A | Global MMLU | 53.5 | 50.54 | 46.8 | 49.5 | 65.1 | Extended Thinking Evaluation results in reasoning mode for SmolLM3 and Qwen3 models: | Category | Metric | SmoLLM3-3B | Qwen3-1.7B | Qwen3-4B | |---------|--------|------------|------------|----------| | High school math competition | AIME 2025 | 36.7 | 30.7 | 58.8 | | Math problem-solving | GSM-Plus | 83.4 | 79.4 | 88.2 | | Competitive programming | LiveCodeBench v4 | 30.0 | 34.4 | 52.9 | | Graduate-level reasoning | GPQA Diamond | 41.7 | 39.9 | 55.3 | | Instruction following | IFEval | 71.2 | 74.2 | 85.4 | | Alignment | MixEval Hard | 30.8 | 33.9 | 38.0 | | Knowledge | MMLU-Pro | 58.4 | 57.8 | 70.2 | | Multilingual Q&A | Global MMLU | 64.1 | 62.3 | 73.3 | - Architecture: Transformer decoder - Pretraining tokens: 11T - Precision: bfloat16 - GPUs: 384 H100 - Training Framework: nanotron - Data processing framework: datatrove - Evaluation framework: lighteval - Post-training Framework: TRL Open resources Here is an infographic with all the training details [TODO]. - The datasets used for pretraining can be found in this collection and those used in mid-training and pos-training can be found here [TODO] - The training and evaluation configs and code can be found in the huggingface/smollm repository. SmolLM3 can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.

Then the endpoint can be accessed at http://localhost:8080/embedding, for example using `curl`: Alternatively, the `llama-embedding` command line tool can be used: embdnormalize When a model uses pooling, or the pooling method is specified using `--pooling`, the normalization can be controlled by the `embdnormalize` parameter. The default value is `2` which means that the embeddings are normalized using the Euclidean norm (L2). Other options are: -1 No normalization 0 Max absolute 1 Taxicab 2 Euclidean/L2 \>2 P-Norm This can be passed in the request body to `llama-server`, for example: And for `llama-embedding`, by passing `--embd-normalize `, for example:

Original model: https://huggingface.co/lightonai/LightOnOCR-1B-1025 Related PR: https://github.com/ggml-org/llama.cpp/pull/16764

[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] Summary description and brief definition of inputs and outputs. 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. - 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 - Total output context of 8192 tokens Data used for model training and how the data was processed. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: - Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages. - Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions. - Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. - Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks. The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats. Here are the key data cleaning and filtering methods applied to the training data: - CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. - Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. - Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: - Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs. - Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. - Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. - Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. - These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: | Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:| | [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 | | [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 | | [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 | | [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 | | [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 | | [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 | | [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 | | [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 | | [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 | | [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 | | [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 | [hellaswag]: https://arxiv.org/abs/1905.07830 [boolq]: https://arxiv.org/abs/1905.10044 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [arc]: https://arxiv.org/abs/1911.01547 [winogrande]: https://arxiv.org/abs/1907.10641 [bbh]: https://paperswithcode.com/dataset/bbh [drop]: https://arxiv.org/abs/1903.00161 | Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |----------------|:-------------:|:--------------:|:--------------:| | [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 | | [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 | | [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 | | [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 | | [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 | | [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 | | [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 | | [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 | [mmlu]: https://arxiv.org/abs/2009.03300 [agieval]: https://arxiv.org/abs/2304.06364 [math]: https://arxiv.org/abs/2103.03874 [gsm8k]: https://arxiv.org/abs/2110.14168 [gpqa]: https://arxiv.org/abs/2311.12022 [mbpp]: https://arxiv.org/abs/2108.07732 [humaneval]: https://arxiv.org/abs/2107.03374 | Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:| | [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 | | [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 | | [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 | | [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 | | [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 | | [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 | | [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 | [mgsm]: https://arxiv.org/abs/2210.03057 [flores]: https://arxiv.org/abs/2106.03193 [xquad]: https://arxiv.org/abs/1910.11856v3 [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite [wmt24pp]: https://arxiv.org/abs/2502.12404v1 [eclektic]: https://arxiv.org/abs/2502.21228 [indicgenbench]: https://arxiv.org/abs/2404.16816 | Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |:-------------:|:--------------:|:--------------:| | [COCOcap][coco-cap] | 102 | 111 | 116 | | [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 | | [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 | | [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 | | [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 | | [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 | | [ReMI][remi] | 27.3 | 38.5 | 44.8 | | [AI2D][ai2d] | 63.2 | 75.2 | 79.0 | | [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 | | [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 | | [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 | | [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 | | [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 | | [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 | | [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 | [coco-cap]: https://cocodataset.org/#home [docvqa]: https://www.docvqa.org/ [info-vqa]: https://arxiv.org/abs/2104.12756 [mmmu]: https://arxiv.org/abs/2311.16502 [textvqa]: https://textvqa.org/ [realworldqa]: https://paperswithcode.com/dataset/realworldqa [remi]: https://arxiv.org/html/2406.09175v1 [ai2d]: https://allenai.org/data/diagrams [chartqa]: https://arxiv.org/abs/2203.10244 [vqav2]: https://visualqa.org/index.html [blinkvqa]: https://arxiv.org/abs/2404.12390 [okvqa]: https://okvqa.allenai.org/ [tallyqa]: https://arxiv.org/abs/1810.12440 [ss-vqa]: https://arxiv.org/abs/1908.02660 [countbenchqa]: https://github.com/google-research/bigvision/blob/main/bigvision/datasets/countbenchqa/ Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: - Child Safety: Evaluation of text-to-text and image to text prompts covering child safety policies, including child sexual abuse and exploitation. - Content Safety: Evaluation of text-to-text and image to text prompts covering safety policies including, harassment, violence and gore, and hate speech. - Representational Harms: Evaluation of text-to-text and image to text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies. In addition to development level evaluations, we conduct "assurance evaluations" which are our 'arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review. For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts. These models have certain limitations that users should be aware of. Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. - Content Creation and Communication - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. - Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. - Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications. - Research and Education - Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field. - Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. - Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. - Training Data - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. - The scope of the training dataset determines the subject areas the model can handle effectively. - Context and Task Complexity - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). - Language Ambiguity and Nuance - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. - Factual Accuracy - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. - Common Sense - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: - Bias and Fairness - VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. - Misinformation and Misuse - VLMs can be misused to generate text that is false, misleading, or harmful. - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. - Transparency and Accountability: - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. - A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. - Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. - Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. - Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of VLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. - Privacy violations: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [g3-tech-report]: https://goo.gle/Gemma3Report [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3 [vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3 [terms]: https://ai.google.dev/gemma/terms [safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf [prohibited-use]: https://ai.google.dev/gemma/prohibitedusepolicy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/jax-ml/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [gemini-2-paper]: https://arxiv.org/abs/2312.11805

Original model: https://huggingface.co/Qwen/Qwen2.5-Omni-7B Modalities: - ✅ Text input - ✅ Audio input - ✅ Image input - ❌ Video input - ❌ Audio generation Ref PR: https://github.com/ggml-org/llama.cpp/pull/13784

Original model: https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct Related PR: https://github.com/ggml-org/llama.cpp/pull/16780

Original model: https://huggingface.co/HuggingFaceTB/SmolVLM2-256M-Video-Instruct For more info, please refer to this PR: https://github.com/ggml-org/llama.cpp/pull/13050

ggml-org/Qwen3-4B-Thinking-2507-Q80-GGUF This model was converted to GGUF format from `Qwen/Qwen3-4B-Thinking-2507` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

> [!Note] > This version does not contain multimodal support. We are still working on adding multimodal. Original model: https://huggingface.co/google/gemma-3n-E2B-it - Responsible Generative AI Toolkit - Gemma on Kaggle - Gemma on HuggingFace - Gemma on Vertex Model Garden To install llama.cpp on your system, see installation guide Search for `gemma-3n-E2B-it-GGUF` and add it to your model library

ggml-org/Qwen3-4B-Instruct-2507-Q80-GGUF This model was converted to GGUF format from `Qwen/Qwen3-4B-Instruct-2507` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

ggml-org/Qwen3-Reranker-0.6B-Q80-GGUF This model was converted to GGUF format from `Qwen/Qwen3-Reranker-0.6B` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

ggml-org/Qwen3-30B-A3B-Instruct-2507-Q80-GGUF This model was converted to GGUF format from `Qwen/Qwen3-30B-A3B-Instruct-2507` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

ggml-org/Qwen3-30B-A3B-Thinking-2507-Q80-GGUF This model was converted to GGUF format from `Qwen/Qwen3-30B-A3B-Thinking-2507` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

> [!IMPORTANT] > NOTE: this model is used for testing Qwen2.5-Coder-1.5B-IQ3XXS-GGUF This model was converted to GGUF format from `Qwen/Qwen2.5-Coder-1.5B` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMACURL=1` flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

Voice Activity Detection (VAD) models for whisper.cpp This repository contains VAD models that have been converted to ggml format for usage with whiper.cpp.