README.md

---
base_model: SicariusSicariiStuff/Impish_LLAMA_3B
language:
- en
license: llama3.2
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/Impish_LLAMA_3B-Q5_K_M-GGUF
This model was converted to GGUF format from [`SicariusSicariiStuff/Impish_LLAMA_3B`](https://huggingface.co/SicariusSicariiStuff/Impish_LLAMA_3B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/SicariusSicariiStuff/Impish_LLAMA_3B) for more details on the model.

---
"With that naughty impish grin of hers, so damn sly it could have 
ensnared the devil himself, and that impish glare in her eyes, sharper 
than of a succubus fang, she chuckled impishly with such mischief that 
even the moon might’ve blushed. I needed no witch's hex to divine her 
nature—she was, without a doubt, a naughty little imp indeed."

Model Details
-
Intended use: Role-Play, General tasks.

Censorship level: Medium - Low

5.5 / 10 (10 completely uncensored)

"I want some legit RP models of LLAMA 3.2 3B, we got phones!"


  "So make one."


  "K."


  This model was trained on ~25M tokens, in 3 phases, the first and longest phase was an FFT to teach the model new stuff, and to confuse the shit out of it too, so it would be a little bit less inclined to use GPTisms.


  It worked pretty well. In fact, the model was so damn thoroughly 
confused, that the little devil didn't even make any sense at all, but 
the knowledge was there.


  In the next phase, a DEEP QLORA of R = 512 was used on a new dataset, to... unconfuse it. A completely different dataset was used to avoid overfitting.


  Finally, another somewhat deep QLORA of R = 128 
was used to tie it all together in a coherent way, and connect all the 
dots, and this was also with a different dataset as well.


  The results are sometimes surprisingly good, it 
even managed to fool some people into thinking it's a MUCH larger model,
 and sometimes... sometimes it behaves just like you would expect a 3B 
model to...


  Fun fact: the model was uploaded while there were 200 ICBMs headed my way, just flying there in the sky.


  I lived, so expect more models in the future!

Model instruction template: Llama-3-Instruct

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{output}<|eot_id|>

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Impish_LLAMA_3B-Q5_K_M-GGUF --hf-file impish_llama_3b-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Impish_LLAMA_3B-Q5_K_M-GGUF --hf-file impish_llama_3b-q5_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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/Impish_LLAMA_3B-Q5_K_M-GGUF --hf-file impish_llama_3b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Impish_LLAMA_3B-Q5_K_M-GGUF --hf-file impish_llama_3b-q5_k_m.gguf -c 2048
```