Triangle104/Llama-3.1-Jamet-8B-MK.I-Q5_K_S-GGUF

This model was converted to GGUF format from Hastagaras/Llama-3.1-Jamet-8B-MK.I using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

---

Model details:

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 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

---

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Llama-3.1-Jamet-8B-MK.I-Q5_K_S-GGUF --hf-file llama-3.1-jamet-8b-mk.i-q5_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Llama-3.1-Jamet-8B-MK.I-Q5_K_S-GGUF --hf-file llama-3.1-jamet-8b-mk.i-q5_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Llama-3.1-Jamet-8B-MK.I-Q5_K_S-GGUF --hf-file llama-3.1-jamet-8b-mk.i-q5_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Llama-3.1-Jamet-8B-MK.I-Q5_K_S-GGUF --hf-file llama-3.1-jamet-8b-mk.i-q5_k_s.gguf -c 2048