Instructions to use temporary0-0name/pretrained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use temporary0-0name/pretrained_model with Transformers:

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

pipe = pipeline("text-generation", model="temporary0-0name/pretrained_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("temporary0-0name/pretrained_model")
model = AutoModelForCausalLM.from_pretrained("temporary0-0name/pretrained_model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use temporary0-0name/pretrained_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "temporary0-0name/pretrained_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "temporary0-0name/pretrained_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/temporary0-0name/pretrained_model

SGLang

How to use temporary0-0name/pretrained_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 "temporary0-0name/pretrained_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": "temporary0-0name/pretrained_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 "temporary0-0name/pretrained_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": "temporary0-0name/pretrained_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use temporary0-0name/pretrained_model with Docker Model Runner:
```
docker model run hf.co/temporary0-0name/pretrained_model
```

temporary0-0name commited on Nov 18, 2023

Commit

47f9fa4

1 Parent(s): e1e4b67

Training in progress, step 100

Browse files

Files changed (8) hide show

config.json +1 -1
model.safetensors +1 -1
runs/Nov18_14-32-49_bdcc79452b0f/events.out.tfevents.1700317972.bdcc79452b0f.1420.0 +3 -0
runs/Nov18_14-35-25_bdcc79452b0f/events.out.tfevents.1700318128.bdcc79452b0f.1420.1 +3 -0
runs/Nov18_14-37-49_bdcc79452b0f/events.out.tfevents.1700318272.bdcc79452b0f.5253.0 +3 -0
runs/Nov18_14-38-39_bdcc79452b0f/events.out.tfevents.1700318325.bdcc79452b0f.5253.1 +3 -0
tokenizer.json +16 -2
training_args.bin +3 -0

config.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
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   "architectures": [
     "BertLMHeadModel"
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 {
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   "architectures": [
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   ],

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