--- base_model: Sao10K/Llama-3.1-8B-Stheno-v3.4 quantized_by: Lewdiculous library_name: transformers license: cc-by-nc-4.0 inference: false language: - en tags: - roleplay - llama3 - sillytavern --- Quants for [**Sao10K/Llama-3.1-8B-Stheno-v3.4**](https://huggingface.co./Sao10K/Llama-3.1-8B-Stheno-v3.4). I recommend checking their page for feedback and support. > [!IMPORTANT] > **Quantization process:**
> Imatrix data was generated from the FP16-GGUF and conversions directly from the BF16-GGUF.
> This hopefully avoids losses during conversion.
> To run this model, please use the [**latest version of KoboldCpp**](https://github.com/LostRuins/koboldcpp/releases/latest).
> If you noticed any issues let me know in the discussions. > [!NOTE] > **Presets:**
> Some compatible SillyTavern presets can be found [**here (Virt's Roleplay Presets - v1.9)**](https://huggingface.co./Virt-io/SillyTavern-Presets).
> Check [**discussions such as this one**](https://huggingface.co./Virt-io/SillyTavern-Presets/discussions/5#664d6fb87c563d4d95151baa) and [**this one**](https://www.reddit.com/r/SillyTavernAI/comments/1dff2tl/my_personal_llama3_stheno_presets/) for other presets and samplers recommendations.
> Lower temperatures are recommended by the authors, so make sure to experiment.
> > **General usage with KoboldCpp:**
> For **8GB VRAM** GPUs, I recommend the **Q4_K_M-imat** (4.89 BPW) quant for up to 12288 context sizes without the use of `--quantkv`.
> Using `--quantkv 1` (≈Q8) or even `--quantkv 2` (≈Q4) can get you to 32K context sizes with the caveat of not being compatible with Context Shifting, only relevant if you can manage to fill up that much context.
> [**Read more about it in the release here**](https://github.com/LostRuins/koboldcpp/releases/tag/v1.67). ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d4cf2693a0a3744a27536c/GV63jjNPXvSG-BSOGuP5h.png)
Click here for the original model card information. ![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