---
base_model: bunnycore/Phi-4-RP-V0.2
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
license: mit
---

# Triangle104/Phi-4-RP-V0.2-Q5_K_M-GGUF
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.
Refer to the [original model card](https://huggingface.co/bunnycore/Phi-4-RP-V0.2) for more details on the model.

---
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

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
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:
```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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
```