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--- |
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license: apache-2.0 |
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base_model: PygmalionAI/Pygmalion-3-12B |
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tags: |
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- roleplay |
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- conversational |
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- llama-cpp |
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- gguf-my-repo |
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--- |
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# Triangle104/Pygmalion-3-12B-Q5_K_S-GGUF |
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This model was converted to GGUF format from [`PygmalionAI/Pygmalion-3-12B`](https://huggingface.co/PygmalionAI/Pygmalion-3-12B) 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/PygmalionAI/Pygmalion-3-12B) for more details on the model. |
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--- |
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Dataset |
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We've gathered a large collection of instructions and roleplaying totaling hundreds of millions of tokens, including our PIPPA dataset and roleplaying forums. |
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Limitations and biases |
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The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope. |
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As such, it was not fine-tuned to be safe and |
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harmless: the base model and this fine-tune have been trained on data |
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known to contain profanity and texts that are lewd or otherwise |
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offensive. It may produce socially unacceptable or undesirable text, |
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even if the prompt itself does not include anything explicitly |
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offensive. Outputs might often be factually wrong or misleading. |
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Training Specifications |
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We trained our model as a rank-32 LoRA adapter with one epoch over |
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our data using 8x NVIDIA A40 GPUs. For this run, we employed a learning |
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rate of 2e-4 and a total batch size across all GPUs of 24. A cosine |
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learning rate scheduler was used with a 100 step warmup. DeepSpeed ZeRO |
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was used to successfully get memory usage down. |
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Acknowledgements |
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This project could not have been done without the compute support of Hive Digital Technologies and the Axolotl training software. |
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We'd like to extensively thank lemonilia for their wonderful help in compiling roleplay forum data. |
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And most of all, we dedicate this model to our great community, |
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who've stuck with us through everything until now. Sincerely, thank you |
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so much. We hope you enjoy our work to the fullest and we promise more |
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is on the way soon. |
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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/Pygmalion-3-12B-Q5_K_S-GGUF --hf-file pygmalion-3-12b-q5_k_s.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/Pygmalion-3-12B-Q5_K_S-GGUF --hf-file pygmalion-3-12b-q5_k_s.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/Pygmalion-3-12B-Q5_K_S-GGUF --hf-file pygmalion-3-12b-q5_k_s.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/Pygmalion-3-12B-Q5_K_S-GGUF --hf-file pygmalion-3-12b-q5_k_s.gguf -c 2048 |
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``` |
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