---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:101762
- loss:TripletLoss
- llama-cpp
- gguf-my-repo
base_model: ravi259/distilroberta-base-sentence-transformer_finetuned
widget:
- source_sentence: A dog is in the water.
  sentences:
  - The woman is wearing green.
  - The dog is rolling around in the grass.
  - A brown dog swims through water outdoors with a tennis ball in its mouth.
- source_sentence: A dog is swimming.
  sentences:
  - a black dog swimming in the water with a tennis ball in his mouth
  - A dog with yellow fur swims, neck deep, in water.
  - A brown dog running through a large orange tube.
- source_sentence: A dog is swimming.
  sentences:
  - A dog with golden hair swims through water.
  - A golden haired dog is lying in a boat that is traveling on a lake.
  - A dog with golden hair swims through water.
- source_sentence: A dog is swimming.
  sentences:
  - A tan dog splashes as he swims through the water.
  - A man and young boy asleep in a chair.
  - A dog in a harness chasing a red ball.
- source_sentence: A dog is in the water.
  sentences:
  - A big brown dog jumps into a swimming pool on the backyard.
  - Wet brown dog swims towards camera.
  - The dog is rolling around in the grass.
datasets:
- embedding-data/QQP_triplets
- sentence-transformers/all-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on distilbert/distilroberta-base
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli dev
      type: all-nli-dev
    metrics:
    - type: cosine_accuracy
      value: 0.9556500315666199
      name: Cosine Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli test
      type: all-nli-test
    metrics:
    - type: cosine_accuracy
      value: 0.9048267602920532
      name: Cosine Accuracy
---

# Sleem247/distilroberta-base-sentence-transformer_finetuned-Q8_0-GGUF
This model was converted to GGUF format from [`ravi259/distilroberta-base-sentence-transformer_finetuned`](https://huggingface.co/ravi259/distilroberta-base-sentence-transformer_finetuned) 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/ravi259/distilroberta-base-sentence-transformer_finetuned) 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 Sleem247/distilroberta-base-sentence-transformer_finetuned-Q8_0-GGUF --hf-file distilroberta-base-sentence-transformer_finetuned-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Sleem247/distilroberta-base-sentence-transformer_finetuned-Q8_0-GGUF --hf-file distilroberta-base-sentence-transformer_finetuned-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 Sleem247/distilroberta-base-sentence-transformer_finetuned-Q8_0-GGUF --hf-file distilroberta-base-sentence-transformer_finetuned-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Sleem247/distilroberta-base-sentence-transformer_finetuned-Q8_0-GGUF --hf-file distilroberta-base-sentence-transformer_finetuned-q8_0.gguf -c 2048
```