Instructions to use ccore/core-prompt-reverser-opt-1.3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ccore/core-prompt-reverser-opt-1.3b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ccore/core-prompt-reverser-opt-1.3b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ccore/core-prompt-reverser-opt-1.3b")
model = AutoModelForMultimodalLM.from_pretrained("ccore/core-prompt-reverser-opt-1.3b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ccore/core-prompt-reverser-opt-1.3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ccore/core-prompt-reverser-opt-1.3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ccore/core-prompt-reverser-opt-1.3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ccore/core-prompt-reverser-opt-1.3b

SGLang

How to use ccore/core-prompt-reverser-opt-1.3b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ccore/core-prompt-reverser-opt-1.3b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ccore/core-prompt-reverser-opt-1.3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ccore/core-prompt-reverser-opt-1.3b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ccore/core-prompt-reverser-opt-1.3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ccore/core-prompt-reverser-opt-1.3b with Docker Model Runner:
```
docker model run hf.co/ccore/core-prompt-reverser-opt-1.3b
```

PREVIEW - training will end 4/9

commit a87a7a188022bec44cffcb3ae9c250b8bacf7dd3 seems to be more stable than the lasts commits, the next one I will post only at 6/9

core-prompt-reverser-opt-1.3b

This model is a fine-tuned version of facebook/opt-1.3b on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.2950
Accuracy: 0.7084

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0

ccore
/

core-prompt-reverser-opt-1.3b

PREVIEW - training will end 4/9

core-prompt-reverser-opt-1.3b

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Datasets used to train ccore/core-prompt-reverser-opt-1.3b