README.md

---
library_name: transformers
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-14B/blob/main/LICENSE
base_model: Sao10K/14B-Qwen2.5-Freya-x1
tags:
- generated_from_trainer
- llama-cpp
- gguf-my-repo
model-index:
- name: 14B-Qwen2.5-Freya-x1
  results: []
---

# Triangle104/14B-Qwen2.5-Freya-x1-Q8_0-GGUF
This model was converted to GGUF format from [`Sao10K/14B-Qwen2.5-Freya-x1`](https://huggingface.co/Sao10K/14B-Qwen2.5-Freya-x1) 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/Sao10K/14B-Qwen2.5-Freya-x1) for more details on the model.

---
Model details:
-
I decided to mess around with training methods again, considering the re-emegence of methods like multi-step training. Some people began doing it again, and so, why not? Inspired by AshhLimaRP's methology but done it my way.

Freya-S1

    LoRA Trained on ~1.1GB of literature and raw text over Qwen 2.5's base model.
    Cleaned text and literature as best as I could, still, may have had issues here and there.

Freya-S2

    The first LoRA was applied over Qwen 2.5 Instruct, then I trained on top of that.
    Reduced LoRA rank because it's mainly instruct and other details I won't get into.

Recommended Model Settings | Look, I just use these, they work fine enough. I don't even know how DRY or other meme samplers work. Your system prompt matters more anyway.

Prompt Format: ChatML
Temperature: 1+ # I don't know, man.
min_p: 0.05

Training time in total was ~10 Hours on a 8xH100 Node, sponsored by the Government of Singapore or something. Thanks for the national service allowance, MHA.

https://sao10k.carrd.co/ for contact.

---
## 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 Triangle104/14B-Qwen2.5-Freya-x1-Q8_0-GGUF --hf-file 14b-qwen2.5-freya-x1-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/14B-Qwen2.5-Freya-x1-Q8_0-GGUF --hf-file 14b-qwen2.5-freya-x1-q8_0.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 Triangle104/14B-Qwen2.5-Freya-x1-Q8_0-GGUF --hf-file 14b-qwen2.5-freya-x1-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/14B-Qwen2.5-Freya-x1-Q8_0-GGUF --hf-file 14b-qwen2.5-freya-x1-q8_0.gguf -c 2048
```