README.md

---
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"
```