Triangle104/Phi-4-RP-V0.2-Q5_K_M-GGUF

This model was converted to GGUF format from bunnycore/Phi-4-RP-V0.2 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:

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.

Primary Use Cases

Interactive Storytelling : Engage users in dynamic, immersive stories where they can take on different roles and make choices that influence the narrative.
Role-Playing Games (RPGs) : Provide rich, interactive experiences in RPGs, enhancing gameplay through intelligent character interactions.
Virtual Assistants : Offer personalized, engaging conversations that simulate human-like interactions for customer support or entertainment purposes.

Training Data

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:

Role-playing game scripts and narratives.
Interactive storytelling scenarios.
Character dialogues and interactions from diverse fictional settings.

Input Formats

Given the nature of the training data, phi-4 is best suited for prompts using the chat format as follows:

<|im_start|>system<|im_sep|> You are a medieval knight and must provide explanations to modern people.<|im_end|> <|im_start|>user<|im_sep|> How should I explain the Internet?<|im_end|> <|im_start|>assistant<|im_sep|>

Merge Method

This model was merged using the passthrough merge method using unsloth/phi-4 + bunnycore/Phi-4-rp-v1-lora as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: unsloth/phi-4+bunnycore/Phi-4-rp-v1-lora dtype: bfloat16 merge_method: passthrough models:

• model: unsloth/phi-4+bunnycore/Phi-4-rp-v1-lora tokenizer_source: unsloth/phi-4

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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/Phi-4-RP-V0.2-Q5_K_M-GGUF --hf-file phi-4-rp-v0.2-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Phi-4-RP-V0.2-Q5_K_M-GGUF --hf-file phi-4-rp-v0.2-q5_k_m.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/Phi-4-RP-V0.2-Q5_K_M-GGUF --hf-file phi-4-rp-v0.2-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Phi-4-RP-V0.2-Q5_K_M-GGUF --hf-file phi-4-rp-v0.2-q5_k_m.gguf -c 2048