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Llama-3.1-8B-Stheno-v3.4-GGUF

GGUF
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conversational
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Llama-3.1-8B-Stheno-v3.4-GGUF
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---

license: cc-by-nc-4.0
datasets:
- Setiaku/Stheno-v3.4-Instruct
- Setiaku/Stheno-3.4-Creative-2
language:
- en

---

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)

# QuantFactory/Llama-3.1-8B-Stheno-v3.4-GGUF
This is quantized version of [Sao10K/Llama-3.1-8B-Stheno-v3.4](https://huggingface.co./Sao10K/Llama-3.1-8B-Stheno-v3.4) created using llama.cpp

# Original Model Card


![img](https://huggingface.co./Sao10K/Llama-3.1-8B-Stheno-v3.4/resolve/main/meneno.jpg)

---

Thanks to Backyard.ai for the compute to train this. :)

---

Llama-3.1-8B-Stheno-v3.4

This model has went through a multi-stage finetuning process.
```
- 1st, over a multi-turn Conversational-Instruct
- 2nd, over a Creative Writing / Roleplay along with some Creative-based Instruct Datasets.
- - Dataset consists of a mixture of Human and Claude Data.
```

Prompting Format:
```
- Use the L3 Instruct Formatting - Euryale 2.1 Preset Works Well
- Temperature + min_p as per usual, I recommend 1.4 Temp + 0.2 min_p.
- Has a different vibe to previous versions. Tinker around.
```

Changes since previous Stheno Datasets:
```
- Included Multi-turn Conversation-based Instruct Datasets to boost multi-turn coherency. # This is a seperate set, not the ones made by Kalomaze and Nopm, that are used in Magnum. They're completely different data.
- Replaced Single-Turn Instruct with Better Prompts and Answers by Claude 3.5 Sonnet and Claude 3 Opus.
- Removed c2 Samples -> Underway of re-filtering and masking to use with custom prefills. TBD
- Included 55% more Roleplaying Examples based of [Gryphe's](https://huggingface.co./datasets/Gryphe/Sonnet3.5-Charcard-Roleplay) Charcard RP Sets. Further filtered and cleaned on.
- Included 40% More Creative Writing Examples.
- Included Datasets Targeting System Prompt Adherence.
- Included Datasets targeting Reasoning / Spatial Awareness.
- Filtered for the usual errors, slop and stuff at the end. Some may have slipped through, but I removed nearly all of it.
```

Personal Opinions:
```
- Llama3.1 was more disappointing, in the Instruct Tune? It felt overbaked, atleast. Likely due to the DPO being done after their SFT Stage.
- Tuning on L3.1 base did not give good results, unlike when I tested with Nemo base. unfortunate.
- Still though, I think I did an okay job. It does feel a bit more distinctive.
- It took a lot of tinkering, like a LOT to wrangle this.
```

Below are some graphs and all for you to observe.

---

`Turn Distribution # 1 Turn is considered as 1 combined Human/GPT pair in a ShareGPT format. 4 Turns means 1 System Row + 8 Human/GPT rows in total.`

![Turn](https://huggingface.co./Sao10K/Llama-3.1-8B-Stheno-v3.4/resolve/main/turns_distribution_bar_graph.png)

`Token Count Histogram # Based on the Llama 3 Tokenizer`

![Turn](https://huggingface.co./Sao10K/Llama-3.1-8B-Stheno-v3.4/resolve/main/token_count_histogram.png)

---

Have a good one.

```
Source Image: https://www.pixiv.net/en/artworks/91689070
```