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README.md

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+---
+base_model: Felladrin/TinyMistral-248M-SFT-v3
+datasets:
+- OpenAssistant/oasst_top1_2023-08-25
+inference: false
+license: apache-2.0
+model_creator: Felladrin
+model_name: TinyMistral-248M-SFT-v3
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- autotrain
+- text-generation
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+widget:
+- text: '<|im_start|>user
+
+    Write the specs of a game about trolls and warriors in a fantasy world.<|im_end|>
+
+    <|im_start|>assistant
+
+    The game is an adventure game that takes place on a planet, where players must
+    explore their unique abilities to survive. Players can use different strategies
+    such as collecting items or trading them for gold or silver coins, but they also
+    need to learn how to deal with obstacles and find new ways to escape.<|im_end|>
+
+    <|im_start|>user
+
+    Could you tell me something curious about the Earth?<|im_end|>
+
+    <|im_start|>assistant
+
+    The planet is a large, rocky world with an atmosphere of 10 billion years old
+    and a surface area around 25 million miles (36 million kilometers) wide.<|im_end|>
+
+    <|im_start|>user
+
+    What are some potential applications for quantum computing?<|im_end|>
+
+    <|im_start|>assistant'
+---
+# Felladrin/TinyMistral-248M-SFT-v3-GGUF
+
+Quantized GGUF model files for [TinyMistral-248M-SFT-v3](https://huggingface.co/Felladrin/TinyMistral-248M-SFT-v3) from [Felladrin](https://huggingface.co/Felladrin)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [tinymistral-248m-sft-v3.fp16.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v3-GGUF/resolve/main/tinymistral-248m-sft-v3.fp16.gguf) | fp16 | 497.75 MB  |
+| [tinymistral-248m-sft-v3.q2_k.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v3-GGUF/resolve/main/tinymistral-248m-sft-v3.q2_k.gguf) | q2_k | 116.20 MB  |
+| [tinymistral-248m-sft-v3.q3_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v3-GGUF/resolve/main/tinymistral-248m-sft-v3.q3_k_m.gguf) | q3_k_m | 131.01 MB  |
+| [tinymistral-248m-sft-v3.q4_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v3-GGUF/resolve/main/tinymistral-248m-sft-v3.q4_k_m.gguf) | q4_k_m | 156.60 MB  |
+| [tinymistral-248m-sft-v3.q5_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v3-GGUF/resolve/main/tinymistral-248m-sft-v3.q5_k_m.gguf) | q5_k_m | 180.16 MB  |
+| [tinymistral-248m-sft-v3.q6_k.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v3-GGUF/resolve/main/tinymistral-248m-sft-v3.q6_k.gguf) | q6_k | 205.20 MB  |
+| [tinymistral-248m-sft-v3.q8_0.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v3-GGUF/resolve/main/tinymistral-248m-sft-v3.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/blob/90b89d18fdf27937dc04ab8a9b543c5af2991c7f/README.md)
+- Dataset: [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25)
+
+## Recommended Prompt Format
+
+```
+<|im_start|>user
+{message}<|im_end|>
+<|im_start|>assistant
+```
+
+## How it was trained
+
+```ipython
+%pip install autotrain-advanced
+
+!autotrain setup
+
+!autotrain llm \
+    --train \
+    --trainer "sft" \
+    --model './TinyMistral-248M/' \
+    --model_max_length 4096 \
+    --block-size 1024 \
+    --project-name 'trained-model' \
+    --data-path "OpenAssistant/oasst_top1_2023-08-25" \
+    --train_split "train" \
+    --valid_split "test" \
+    --text-column "text" \
+    --lr 1e-5 \
+    --train_batch_size 2 \
+    --epochs 5 \
+    --evaluation_strategy "steps" \
+    --save-strategy "steps" \
+    --save-total-limit 2 \
+    --warmup-ratio 0.05 \
+    --weight-decay 0.0 \
+    --gradient-accumulation 8 \
+    --logging-steps 10 \
+    --scheduler "constant"
+```