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---
license: apache-2.0
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
---
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PROUDLY PRESENTS
```
# mini-magnum-12b-v1.1-exl2-longcal
Quantized using 115 rows of 8192 tokens from the default ExLlamav2-calibration dataset.
Branches:
- `main` -- `measurement.json`
- `8b8h` -- 8bpw, 8bit lm_head
- `6b8h` -- 6bpw, 8bit lm_head
- `4b6h` -- 4bpw, 6bit lm_head
- `2.25b6h` -- 2.25bpw, 6bit lm_head
Original model link: [intervitens/mini-magnum-12b-v1.1](https://huggingface.co/intervitens/mini-magnum-12b-v1.1)
### Quanter's notes
As apparently the default dataset is supposed to be better in nearly all situations, I decided to start quanting using that in addition to my standard rpcal-fare. I'd appreciate real-world tests to confirm the hypothesis, though, so please leave a comment if you find this mode of quanting better than rpcal.
Original model README below.
-----
![](mini-magnum.png)
This model is the miniature version of [alpindale/magnum-72b-v1](https://huggingface.co/alpindale/magnum-72b-v1), a second entry in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is fine-tuned on top of [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407).
A new general purpose instruction dataset by kalomaze was added to the training mix for better coherence and general alignment. We are working on improving our dataset and training procedures, so expect new versions to come out soon.
## Prompting
Model has been Instruct tuned with the Mistral formatting. A typical input would look like this:
```py
"""[INST] Hi there! [/INST]Nice to meet you!</s>[INST] Can I ask a question? [/INST]
"""
```
## Credits
This model has been a team effort, credits go to:
- [Sao10K](https://huggingface.co/Sao10K) and [kalomaze](https://huggingface.co/kalomaze) for help with (and cleaning up!) the dataset.
- [alpindale](https://huggingface.co/alpindale) for the training.
- Various other people for their continued help as we tuned the parameters, restarted failed runs. In no particular order: [Doctor Shotgun](https://huggingface.co/Doctor-Shotgun), [Lucy](https://huggingface.co/lucyknada), [Nopm](https://huggingface.co/nopm), [Mango](https://huggingface.co/MangoMango69420), [Intervitens](https://huggingface.co/intervitens), and the rest of the Silly Tilly.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## Safety
...