Instructions to use Entj/rec_model_correspondence_synth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Entj/rec_model_correspondence_synth with Transformers:

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

pipe = pipeline("image-text-to-text", model="Entj/rec_model_correspondence_synth")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Entj/rec_model_correspondence_synth")
model = AutoModelForMultimodalLM.from_pretrained("Entj/rec_model_correspondence_synth", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Entj/rec_model_correspondence_synth with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Entj/rec_model_correspondence_synth

SGLang

How to use Entj/rec_model_correspondence_synth 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 "Entj/rec_model_correspondence_synth" \
    --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": "Entj/rec_model_correspondence_synth",
		"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 "Entj/rec_model_correspondence_synth" \
        --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": "Entj/rec_model_correspondence_synth",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Entj/rec_model_correspondence_synth with Docker Model Runner:
```
docker model run hf.co/Entj/rec_model_correspondence_synth
```

rec_model_correspondence_synth

File size: 250 Bytes

f15a3c8

{
  "bos_token_id": 0,
  "decoder_start_token_id": 0,
  "early_stopping": true,
  "eos_token_id": 2,
  "length_penalty": 2,
  "max_length": 140,
  "no_repeat_ngram_size": 3,
  "num_beams": 3,
  "pad_token_id": 1,
  "transformers_version": "4.46.2"
}