Test model, the base is llama 3.1 instruct abliterated. Context limit unknown
System:
### Roleplay Instructions
- Be {{char}}, naturally and consistently
- React realistically to {{user}}, never control their actions
- Stay in character at all times
or something similar, just make sure to add: ### Roleplay Instructions
this model is uncensored, maybe too much... in RP scenario (for me)
dataset:
- C2logs that I cleaned a long time ago
- Freedom RP, but it seems it’s already removed from HF
- Stories from Reddit
- Gemma data from: argilla-warehouse/magpie-ultra-v1.0-gemma, just a small subset
- 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.”
- Toxic data: NobodyExistsOnTheInternet/ToxicQAFinal to make it toxic
- And lastly, just my dump—RP, general, etc., with some of it also generated by Gemini.
so yeah, most of the data is from Google, and only the RP data is from Claude.
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
Feedback is greatly appreciated for future improvements (hopefully)
Technical Details:
Base model
v
finetuned the lm_head, embed_tokens and first layer (0)
v
finetune it again, layer 1-2
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again, but this time using Lora, 64 rank
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then merge the lora
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the abliterated instruct
v
same, finetuned the lm_head, embed_tokens and first layer (0)
v
still the same, finetune it again, layer 1-2
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finetune middle layers
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merged the previous Lora with this finetuned abliterated model
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finnaly, merge the two model using ties
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 25.40 |
IFEval (0-Shot) | 73.38 |
BBH (3-Shot) | 29.50 |
MATH Lvl 5 (4-Shot) | 12.54 |
GPQA (0-shot) | 3.24 |
MuSR (0-shot) | 6.14 |
MMLU-PRO (5-shot) | 27.58 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard73.380
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard29.500
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard12.540
- acc_norm on GPQA (0-shot)Open LLM Leaderboard3.240
- acc_norm on MuSR (0-shot)Open LLM Leaderboard6.140
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard27.580