---
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
base_model: bunnycore/Phi-3.5-mini-TitanFusion-0.1
model-index:
- name: Phi-3.5-mini-TitanFusion-0.1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 52.28
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Phi-3.5-mini-TitanFusion-0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 35.45
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Phi-3.5-mini-TitanFusion-0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 6.19
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Phi-3.5-mini-TitanFusion-0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 10.85
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Phi-3.5-mini-TitanFusion-0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 15.8
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Phi-3.5-mini-TitanFusion-0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 31.18
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Phi-3.5-mini-TitanFusion-0.1
      name: Open LLM Leaderboard
---

# Triangle104/Phi-3.5-mini-TitanFusion-0.1-Q6_K-GGUF
This model was converted to GGUF format from [`bunnycore/Phi-3.5-mini-TitanFusion-0.1`](https://huggingface.co/bunnycore/Phi-3.5-mini-TitanFusion-0.1) 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-3.5-mini-TitanFusion-0.1) for more details on the model.

---
Model details:
-
This is a merged pre-trained language model created using the TIES merge method. It is based on the microsoft/Phi-3.5-mini-instruct model and incorporates the knowledge and capabilities of the nbeerbower/phi3.5-gutenberg-4B and ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1 models.

Capabilities:
Roleplay: The model can engage in role-playing scenarios, taking on different personas and responding to prompts in a character-appropriate manner.
Creative Writing: It can assist in creative writing tasks, such as brainstorming ideas, generating plotlines, or developing characters.
Reasoning: The model can reason about information and draw conclusions based on the data it has been trained on.
This is a merge of pre-trained language models created using mergekit.

Merge Details
Merge Method
This model was merged using the TIES merge method using microsoft/Phi-3.5-mini-instruct as a base.

Models Merged
The following models were included in the merge:

nbeerbower/phi3.5-gutenberg-4B
ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1
Configuration
The following YAML configuration was used to produce this model:

models:
  - model: ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1
    parameters:
      weight: 1
  - model: nbeerbower/phi3.5-gutenberg-4B
    parameters:
      weight: 1

merge_method: ties
base_model: microsoft/Phi-3.5-mini-instruct
parameters:
  density: 1
  normalize: true
  int8_mask: true
dtype: bfloat16

---
## 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-3.5-mini-TitanFusion-0.1-Q6_K-GGUF --hf-file phi-3.5-mini-titanfusion-0.1-q6_k.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Phi-3.5-mini-TitanFusion-0.1-Q6_K-GGUF --hf-file phi-3.5-mini-titanfusion-0.1-q6_k.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-3.5-mini-TitanFusion-0.1-Q6_K-GGUF --hf-file phi-3.5-mini-titanfusion-0.1-q6_k.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Phi-3.5-mini-TitanFusion-0.1-Q6_K-GGUF --hf-file phi-3.5-mini-titanfusion-0.1-q6_k.gguf -c 2048
```