Instructions to use Shuu12121/CodeEncoderDecoderModel-Ghost-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shuu12121/CodeEncoderDecoderModel-Ghost-large with Transformers:

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

pipe = pipeline("text-generation", model="Shuu12121/CodeEncoderDecoderModel-Ghost-large")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Shuu12121/CodeEncoderDecoderModel-Ghost-large")
model = AutoModelForSeq2SeqLM.from_pretrained("Shuu12121/CodeEncoderDecoderModel-Ghost-large")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Shuu12121/CodeEncoderDecoderModel-Ghost-large with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shuu12121/CodeEncoderDecoderModel-Ghost-large"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shuu12121/CodeEncoderDecoderModel-Ghost-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Shuu12121/CodeEncoderDecoderModel-Ghost-large

SGLang

How to use Shuu12121/CodeEncoderDecoderModel-Ghost-large 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 "Shuu12121/CodeEncoderDecoderModel-Ghost-large" \
    --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": "Shuu12121/CodeEncoderDecoderModel-Ghost-large",
		"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 "Shuu12121/CodeEncoderDecoderModel-Ghost-large" \
        --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": "Shuu12121/CodeEncoderDecoderModel-Ghost-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Shuu12121/CodeEncoderDecoderModel-Ghost-large with Docker Model Runner:
```
docker model run hf.co/Shuu12121/CodeEncoderDecoderModel-Ghost-large
```

CodeEncoderDecoderModel-Ghost-large

File size: 964 Bytes

d9c2f51

{
  "bos_token": {
    "content": "<s>",
    "lstrip": false,
    "normalized": false,
    "rstrip": false,
    "single_word": false
  },
  "cls_token": {
    "content": "<s>",
    "lstrip": false,
    "normalized": false,
    "rstrip": false,
    "single_word": false
  },
  "eos_token": {
    "content": "</s>",
    "lstrip": false,
    "normalized": false,
    "rstrip": false,
    "single_word": false
  },
  "mask_token": {
    "content": "<mask>",
    "lstrip": true,
    "normalized": false,
    "rstrip": false,
    "single_word": false
  },
  "pad_token": {
    "content": "[PAD]",
    "lstrip": false,
    "normalized": false,
    "rstrip": false,
    "single_word": false
  },
  "sep_token": {
    "content": "</s>",
    "lstrip": false,
    "normalized": false,
    "rstrip": false,
    "single_word": false
  },
  "unk_token": {
    "content": "<unk>",
    "lstrip": false,
    "normalized": false,
    "rstrip": false,
    "single_word": false
  }
}