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--- |
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library_name: transformers |
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license: other |
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license_name: qwen |
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license_link: https://huggingface.co/Qwen/Qwen2.5-14B/blob/main/LICENSE |
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base_model: Sao10K/14B-Qwen2.5-Freya-x1 |
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tags: |
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- generated_from_trainer |
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- llama-cpp |
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- gguf-my-repo |
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model-index: |
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- name: 14B-Qwen2.5-Freya-x1 |
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results: [] |
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--- |
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# Triangle104/14B-Qwen2.5-Freya-x1-Q8_0-GGUF |
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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. |
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Refer to the [original model card](https://huggingface.co/Sao10K/14B-Qwen2.5-Freya-x1) for more details on the model. |
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--- |
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Model details: |
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- |
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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. |
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Freya-S1 |
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LoRA Trained on ~1.1GB of literature and raw text over Qwen 2.5's base model. |
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Cleaned text and literature as best as I could, still, may have had issues here and there. |
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Freya-S2 |
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The first LoRA was applied over Qwen 2.5 Instruct, then I trained on top of that. |
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Reduced LoRA rank because it's mainly instruct and other details I won't get into. |
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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. |
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Prompt Format: ChatML |
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Temperature: 1+ # I don't know, man. |
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min_p: 0.05 |
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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. |
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https://sao10k.carrd.co/ for contact. |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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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" |
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``` |
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### Server: |
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```bash |
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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 |
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``` |
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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. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./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" |
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``` |
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or |
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``` |
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./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 |
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``` |
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