Instructions to use Bainbridge/dialogpt-medium-no_ear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bainbridge/dialogpt-medium-no_ear with Transformers:

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

pipe = pipeline("text-generation", model="Bainbridge/dialogpt-medium-no_ear")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bainbridge/dialogpt-medium-no_ear")
model = AutoModelForCausalLM.from_pretrained("Bainbridge/dialogpt-medium-no_ear")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Bainbridge/dialogpt-medium-no_ear with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Bainbridge/dialogpt-medium-no_ear

SGLang

How to use Bainbridge/dialogpt-medium-no_ear 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 "Bainbridge/dialogpt-medium-no_ear" \
    --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": "Bainbridge/dialogpt-medium-no_ear",
		"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 "Bainbridge/dialogpt-medium-no_ear" \
        --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": "Bainbridge/dialogpt-medium-no_ear",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Bainbridge/dialogpt-medium-no_ear with Docker Model Runner:
```
docker model run hf.co/Bainbridge/dialogpt-medium-no_ear
```

Update README.md

by cherry0328 - opened Jan 6

base: refs/heads/main

←

from: refs/pr/2

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@@ -5,6 +5,8 @@ tags:
 model-index:
 - name: dialogpt-medium-no_ear
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -145,4 +147,4 @@ The following hyperparameters were used during training:
 - Transformers 4.27.4
 - Pytorch 1.11.0+cu113
 - Datasets 2.11.0
-- Tokenizers 0.12.1

 model-index:
 - name: dialogpt-medium-no_ear
   results: []
+base_model:
+- microsoft/DialoGPT-medium
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 - Transformers 4.27.4
 - Pytorch 1.11.0+cu113
 - Datasets 2.11.0
+- Tokenizers 0.12.1