# Felladrin/TinyMistral-248M-SFT-v4-GGUF

Quantized GGUF model files for [TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/TinyMistral-248M-SFT-v4) from [Felladrin](https://huggingface.co/Felladrin)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinymistral-248m-sft-v4.fp16.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.fp16.gguf) | fp16 | 497.75 MB  |
| [tinymistral-248m-sft-v4.q2_k.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q2_k.gguf) | q2_k | 116.20 MB  |
| [tinymistral-248m-sft-v4.q3_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q3_k_m.gguf) | q3_k_m | 131.01 MB  |
| [tinymistral-248m-sft-v4.q4_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q4_k_m.gguf) | q4_k_m | 156.60 MB  |
| [tinymistral-248m-sft-v4.q5_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q5_k_m.gguf) | q5_k_m | 180.16 MB  |
| [tinymistral-248m-sft-v4.q6_k.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q6_k.gguf) | q6_k | 205.20 MB  |
| [tinymistral-248m-sft-v4.q8_0.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q8_0.gguf) | q8_0 | 265.26 MB  |



## Original Model Card:
# Locutusque's TinyMistral-248M trained on OpenAssistant TOP-1 Conversation Threads

- Base model: [Locutusque/TinyMistral-248M](https://huggingface.co/Locutusque/TinyMistral-248M)
- Dataset: [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25)
- Availability in other ML formats:
  - GGUF: [Felladrin/gguf-TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/gguf-TinyMistral-248M-SFT-v4)
  - ONNX: [Felladrin/onnx-TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/onnx-TinyMistral-248M-SFT-v4)

## Recommended Prompt Format

```
<|im_start|>user
{message}<|im_end|>
<|im_start|>assistant
```
{"license": "apache-2.0", "tags": ["text-generation", "gguf", "ggml", "quantized", "q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k", "q8_0"], "datasets": ["OpenAssistant/oasst_top1_2023-08-25"], "model_name": "TinyMistral-248M-SFT-v4", "base_model": "Felladrin/TinyMistral-248M-SFT-v4", "inference": false, "model_creator": "Felladrin", "pipeline_tag": "text-generation", "quantized_by": "afrideva", "widget": [{"text": "<|im_start|>user\nInvited some friends to come home today. Give me some ideas for games to play with them!<|im_end|>\n<|im_start|>assistant"}, {"text": "<|im_start|>user\nHow do meteorologists predict how much air pollution will be produced in the next year?<|im_end|>\n<|im_start|>assistant"}, {"text": "<|im_start|>user\nWho is Mona Lisa?<|im_end|>\n<|im_start|>assistant"}, {"text": "<|im_start|>user\nHeya!<|im_end|>\n<|im_start|>assistant\nHi! How may I help you today?<|im_end|>\n<|im_start|>user\nI need to build a simple website. Where should I start learning about web development?<|im_end|>\n<|im_start|>assistant"}, {"text": "<|im_start|>user\nWhat are some potential applications for quantum computing?<|im_end|>\n<|im_start|>assistant"}, {"text": "<|im_start|>user\nWrite the specs of a game about dragons and warriors in a fantasy world.<|im_end|>\n<|im_start|>assistant"}, {"text": "<|im_start|>user\nGot a question for you!<|im_end|>\n<|im_start|>assistant\nSure! What's it?<|im_end|>\n<|im_start|>user\nWhy do you love cats so much!? \ud83d\udc08<|im_end|>\n<|im_start|>assistant"}, {"text": "<|im_start|>user\nTell me about the pros and cons of social media.<|im_end|>\n<|im_start|>assistant"}]}
gguf text-generation ggml quantized q2_k q3_k_m q4_k_m q5_k_m q6_k q8_0 dataset:OpenAssistant/oasst_top1_2023-08-25 base_model:Felladrin/TinyMistral-248M-SFT-v4 license:apache-2.0 region:us