File size: 3,604 Bytes
95c33bb 3136542 95c33bb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
---
base_model: bunnycore/Phi-4-RP-V0.2
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Triangle104/Phi-4-RP-V0.2-Q8_0-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-Q8_0-GGUF --hf-file phi-4-rp-v0.2-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
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
```
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-Q8_0-GGUF --hf-file phi-4-rp-v0.2-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./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
```
|