Triangle104
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README.md
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@@ -12,6 +12,62 @@ base_model: Hastagaras/Llama-3.1-Jamet-8B-MK.I
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This model was converted to GGUF format from [`Hastagaras/Llama-3.1-Jamet-8B-MK.I`](https://huggingface.co/Hastagaras/Llama-3.1-Jamet-8B-MK.I) 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/Hastagaras/Llama-3.1-Jamet-8B-MK.I) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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This model was converted to GGUF format from [`Hastagaras/Llama-3.1-Jamet-8B-MK.I`](https://huggingface.co/Hastagaras/Llama-3.1-Jamet-8B-MK.I) 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/Hastagaras/Llama-3.1-Jamet-8B-MK.I) for more details on the model.
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---
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Model details:
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-
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System:
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### Roleplay Instructions
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- Be {{char}}, naturally and consistently
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- React realistically to {{user}}, never control their actions
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- Stay in character at all times
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or something similar, just make sure to add: ### Roleplay Instructions
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this model is uncensored, maybe too much... in RP scenario (for me)
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dataset:
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C2logs that I cleaned a long time ago
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Freedom RP, but it seems it’s already removed from HF
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Stories from Reddit
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Gemma data from: argilla-warehouse/magpie-ultra-v1.0-gemma, just a small subset
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Reflection data, from here: PJMixers-Dev/Weyaxi_HelpSteer-filtered-Reflection-Gemini-1.5-Flash-ShareGPT. It’s generated by Gemini, and I was like, “Oh, I can make a Google-themed model with this and Gemma data.”
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Toxic data: NobodyExistsOnTheInternet/ToxicQAFinal to make it toxic
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And lastly, just my dump—RP, general, etc., with some of it also generated by Gemini.
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so yeah, most of the data is from Google, and only the RP data is from Claude.
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you can expect some differences in terms of style (a lot of markdown), but don’t expect this model to be as smart as the instruct
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Feedback is greatly appreciated for future improvements (hopefully)
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Technical Details:
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Base model
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v
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finetuned the lm_head, embed_tokens and first layer (0)
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v
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finetune it again, layer 1-2
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v
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again, but this time using Lora, 64 rank
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v
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then merge the lora
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---
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the abliterated instruct
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v
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same, finetuned the lm_head, embed_tokens and first layer (0)
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v
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still the same, finetune it again, layer 1-2
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v
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finetune middle layers
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v
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merged the previous Lora with this finetuned abliterated model
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---
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finnaly, merge the two model using ties
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---
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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