YAML Metadata
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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Produced by Antigma Labs, Antigma Quantize Space
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Antigma's GitHub Homepage https://github.com/AntigmaLabs
llama.cpp quantization
Using llama.cpp release b5223 for quantization. Original model: https://huggingface.co/unsloth/Devstral-Small-2505 Run them directly with llama.cpp, or any other llama.cpp based project
Prompt format
<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜>
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split |
|---|---|---|---|
| devstral-small-2505-q2_k.gguf | Q2_K | 8.28 GB | False |
| devstral-small-2505-q3_k_l.gguf | Q3_K_L | 11.55 GB | False |
| devstral-small-2505-q6_k.gguf | Q6_K | 18.02 GB | False |
| devstral-small-2505-q4_k_m.gguf | Q4_K_M | 13.35 GB | False |
| devstral-small-2505-q5_k_m.gguf | Q5_K_M | 15.61 GB | False |
| devstral-small-2505-q8_0.gguf | Q8_0 | 23.33 GB | False |
Downloading using huggingface-cli
Click to view download instructions
First, make sure you have hugginface-cli installed:pip install -U "huggingface_hub[cli]"
Then, you can target the specific file you want:
huggingface-cli download https://huggingface.co/Antigma/Devstral-Small-2505-GGUF --include "devstral-small-2505-q2_k.gguf" --local-dir ./
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
huggingface-cli download https://huggingface.co/Antigma/Devstral-Small-2505-GGUF --include "devstral-small-2505-q2_k.gguf/*" --local-dir ./
You can either specify a new local-dir (deepseek-ai_DeepSeek-V3-0324-Q8_0) or download them all in place (./)
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Model tree for Antigma/Devstral-Small-2505-GGUF
Base model
mistralai/Mistral-Small-3.1-24B-Base-2503
Finetuned
mistralai/Devstral-Small-2505
Finetuned
unsloth/Devstral-Small-2505