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--- |
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base_model: bunnycore/Phi-4-RP-V0.2 |
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library_name: transformers |
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tags: |
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- mergekit |
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- merge |
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- llama-cpp |
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- gguf-my-repo |
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--- |
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# Triangle104/Phi-4-RP-V0.2-Q8_0-GGUF |
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This model was converted to GGUF format from [`bunnycore/Phi-4-RP-V0.2`](https://huggingface.co/bunnycore/Phi-4-RP-V0.2) 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/bunnycore/Phi-4-RP-V0.2) for more details on the model. |
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--- |
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Model details: |
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- |
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Phi-4-RP-V0.2 is based on the Phi-4 architecture, which is a state-of-the-art large language model designed to handle a wide range of natural language tasks with high efficiency and performance. |
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Primary Use Cases |
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Interactive Storytelling : Engage users in dynamic, immersive stories where they can take on different roles and make choices that influence the narrative. |
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Role-Playing Games (RPGs) : Provide rich, interactive experiences in RPGs, enhancing gameplay through intelligent character interactions. |
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Virtual Assistants : Offer personalized, engaging conversations that simulate human-like interactions for customer support or entertainment purposes. |
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Training Data |
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Phi-4-RP-V0.2 is specifically trained on role-playing datasets to ensure comprehensive understanding and versatility in various role-playing contexts. This includes but is not limited to: |
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Role-playing game scripts and narratives. |
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Interactive storytelling scenarios. |
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Character dialogues and interactions from diverse fictional settings. |
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Input Formats |
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Given the nature of the training data, phi-4 is best suited for prompts using the chat format as follows: |
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<|im_start|>system<|im_sep|> |
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You are a medieval knight and must provide explanations to modern people.<|im_end|> |
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<|im_start|>user<|im_sep|> |
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How should I explain the Internet?<|im_end|> |
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<|im_start|>assistant<|im_sep|> |
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Merge Method |
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This model was merged using the passthrough merge method using unsloth/phi-4 + bunnycore/Phi-4-rp-v1-lora as a base. |
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Models Merged |
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The following models were included in the merge: |
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Configuration |
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The following YAML configuration was used to produce this model: |
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base_model: unsloth/phi-4+bunnycore/Phi-4-rp-v1-lora |
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dtype: bfloat16 |
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merge_method: passthrough |
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models: |
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- model: unsloth/phi-4+bunnycore/Phi-4-rp-v1-lora |
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tokenizer_source: unsloth/phi-4 |
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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/Phi-4-RP-V0.2-Q8_0-GGUF --hf-file phi-4-rp-v0.2-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/Phi-4-RP-V0.2-Q8_0-GGUF --hf-file phi-4-rp-v0.2-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/Phi-4-RP-V0.2-Q8_0-GGUF --hf-file phi-4-rp-v0.2-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/Phi-4-RP-V0.2-Q8_0-GGUF --hf-file phi-4-rp-v0.2-q8_0.gguf -c 2048 |
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``` |
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