DarqueDante
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README.md
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- gguf-my-repo
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---
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# DarqueDante/Llama-3.1-8B-Stheno-v3.4-Q6_K-GGUF
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This model was converted to GGUF format from [`Sao10K/Llama-3.1-8B-Stheno-v3.4`](https://huggingface.co/Sao10K/Llama-3.1-8B-Stheno-v3.4) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/Sao10K/Llama-3.1-8B-Stheno-v3.4) for more details on the model.
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![img](https://huggingface.co/Sao10K/Llama-3.1-8B-Stheno-v3.4/resolve/main/meneno.jpg)
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---
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Llama-3.1-8B-Stheno-v3.4
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This model has went through a multi-stage finetuning process.
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```
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- 1st, over a multi-turn Conversational-Instruct
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- 2nd, over a Creative Writing / Roleplay along with some Creative-based Instruct Datasets.
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- - Dataset consists of a mixture of Human and Claude Data.
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```
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Prompting Format:
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```
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- Use the L3 Instruct Formatting - Euryale 2.1 Preset Works Well
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- Temperature + min_p as per usual, I recommend 1.4 Temp + 0.2 min_p.
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- Has a different vibe to previous versions. Tinker around.
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```
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Changes since previous Stheno Datasets:
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```
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- 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.
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- Replaced Single-Turn Instruct with Better Prompts and Answers by Claude 3.5 Sonnet and Claude 3 Opus.
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- Removed c2 Samples -> Underway of re-filtering and masking to use with custom prefills. TBD
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- 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.
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- Included 40% More Creative Writing Examples.
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- Included Datasets Targeting System Prompt Adherence.
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- Included Datasets targeting Reasoning / Spatial Awareness.
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- Filtered for the usual errors, slop and stuff at the end. Some may have slipped through, but I removed nearly all of it.
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```
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Personal Opinions:
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```
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- Llama3.1 was more disappointing, in the Instruct Tune? It felt overbaked, atleast. Likely due to the DPO being done after their SFT Stage.
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- Tuning on L3.1 base did not give good results, unlike when I tested with Nemo base. unfortunate.
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- Still though, I think I did an okay job. It does feel a bit more distinctive.
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- It took a lot of tinkering, like a LOT to wrangle this.
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```
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Below are some graphs and all for you to observe.
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---
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`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.`
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![Turn](https://huggingface.co/Sao10K/Llama-3.1-8B-Stheno-v3.4/resolve/main/turns_distribution_bar_graph.png)
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`Token Count Histogram # Based on the Llama 3 Tokenizer`
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![Turn](https://huggingface.co/Sao10K/Llama-3.1-8B-Stheno-v3.4/resolve/main/token_count_histogram.png)
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---
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```
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Source Image: https://www.pixiv.net/en/artworks/91689070
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```
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# DarqueDante/Llama-3.1-8B-Stheno-v3.4-Q6_K-GGUF
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This model was converted to GGUF format from [`Sao10K/Llama-3.1-8B-Stheno-v3.4`](https://huggingface.co/Sao10K/Llama-3.1-8B-Stheno-v3.4) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/Sao10K/Llama-3.1-8B-Stheno-v3.4) for more details on the model.
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