Instructions to use Rasi1610/DeathformInferenceprocessing_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rasi1610/DeathformInferenceprocessing_model with Transformers:

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

pipe = pipeline("image-text-to-text", model="Rasi1610/DeathformInferenceprocessing_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("Rasi1610/DeathformInferenceprocessing_model")
model = AutoModelForImageTextToText.from_pretrained("Rasi1610/DeathformInferenceprocessing_model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Rasi1610/DeathformInferenceprocessing_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rasi1610/DeathformInferenceprocessing_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rasi1610/DeathformInferenceprocessing_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Rasi1610/DeathformInferenceprocessing_model

SGLang

How to use Rasi1610/DeathformInferenceprocessing_model 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 "Rasi1610/DeathformInferenceprocessing_model" \
    --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": "Rasi1610/DeathformInferenceprocessing_model",
		"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 "Rasi1610/DeathformInferenceprocessing_model" \
        --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": "Rasi1610/DeathformInferenceprocessing_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Rasi1610/DeathformInferenceprocessing_model with Docker Model Runner:
```
docker model run hf.co/Rasi1610/DeathformInferenceprocessing_model
```

Rasi1610 commited on Dec 20, 2023

Commit

3720425

1 Parent(s): 9428f7e

Training in progress, epoch 0

Browse files

Files changed (3) hide show

config.json +2 -2
generation_config.json +1 -1
model.safetensors +2 -2

config.json CHANGED Viewed

@@ -85,9 +85,9 @@
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     "use_cache": true,
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   "encoder": {
     "_name_or_path": "",
     "add_cross_attention": false,

     "typical_p": 1.0,
     "use_bfloat16": false,
     "use_cache": true,
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+  "decoder_start_token_id": 57557,
   "encoder": {
     "_name_or_path": "",
     "add_cross_attention": false,

generation_config.json CHANGED Viewed

@@ -1,7 +1,7 @@
 {
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   "max_length": 768,

 {
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   "bos_token_id": 0,
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   "eos_token_id": 2,
   "forced_eos_token_id": 2,
   "max_length": 768,

model.safetensors CHANGED Viewed

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