Triangle104/14B-Qwen2.5-Freya-x1-Q6_K-GGUF

This model was converted to GGUF format from Sao10K/14B-Qwen2.5-Freya-x1 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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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.

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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 Triangle104/14B-Qwen2.5-Freya-x1-Q6_K-GGUF --hf-file 14b-qwen2.5-freya-x1-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/14B-Qwen2.5-Freya-x1-Q6_K-GGUF --hf-file 14b-qwen2.5-freya-x1-q6_k.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 Triangle104/14B-Qwen2.5-Freya-x1-Q6_K-GGUF --hf-file 14b-qwen2.5-freya-x1-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/14B-Qwen2.5-Freya-x1-Q6_K-GGUF --hf-file 14b-qwen2.5-freya-x1-q6_k.gguf -c 2048