---
license: llama3.1
library_name: transformers
tags:
- not-for-all-audiences
model-index:
- name: Llama-3.1-Jamet-8B-MK.I
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 73.38
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Hastagaras/Llama-3.1-Jamet-8B-MK.I
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 29.5
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Hastagaras/Llama-3.1-Jamet-8B-MK.I
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 12.54
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Hastagaras/Llama-3.1-Jamet-8B-MK.I
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 3.24
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Hastagaras/Llama-3.1-Jamet-8B-MK.I
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.14
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Hastagaras/Llama-3.1-Jamet-8B-MK.I
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 27.58
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Hastagaras/Llama-3.1-Jamet-8B-MK.I
      name: Open LLM Leaderboard
---

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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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](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
v
again, but this time using Lora, 64 rank
v
then merge the lora
---
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
v
finetune middle layers
v
merged the previous Lora with this finetuned abliterated model
---
finnaly, merge the two model using ties
```


# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Hastagaras__Llama-3.1-Jamet-8B-MK.I)

|      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|