gguf generated_from_trainer llama-cpp gguf-my-repo dataset:emotion base_model:02shanky/finetuned-twitter-xlm-roberta-base-emotion base_model:quantized:02shanky/finetuned-twitter-xlm-roberta-base-emotion model-index endpoints_compatible region:us feature-extraction
---
base_model: 02shanky/finetuned-twitter-xlm-roberta-base-emotion
datasets:
- emotion
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
- llama-cpp
- gguf-my-repo
model-index:
- name: finetuned-twitter-xlm-roberta-base-emotion
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.9305
      name: Accuracy
    - type: f1
      value: 0.9306713707413102
      name: F1
---

# pizzasan/finetuned-twitter-xlm-roberta-base-emotion-Q8_0-GGUF
This model was converted to GGUF format from [`02shanky/finetuned-twitter-xlm-roberta-base-emotion`](https://huggingface.co/02shanky/finetuned-twitter-xlm-roberta-base-emotion) 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/02shanky/finetuned-twitter-xlm-roberta-base-emotion) for more details on the model.

## 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 pizzasan/finetuned-twitter-xlm-roberta-base-emotion-Q8_0-GGUF --hf-file finetuned-twitter-xlm-roberta-base-emotion-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo pizzasan/finetuned-twitter-xlm-roberta-base-emotion-Q8_0-GGUF --hf-file finetuned-twitter-xlm-roberta-base-emotion-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 pizzasan/finetuned-twitter-xlm-roberta-base-emotion-Q8_0-GGUF --hf-file finetuned-twitter-xlm-roberta-base-emotion-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo pizzasan/finetuned-twitter-xlm-roberta-base-emotion-Q8_0-GGUF --hf-file finetuned-twitter-xlm-roberta-base-emotion-q8_0.gguf -c 2048
```
