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

Libraries

How to use sunilrufus/Emotion_support_chatbot with Transformers:

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

pipe = pipeline("text-generation", model="sunilrufus/Emotion_support_chatbot")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sunilrufus/Emotion_support_chatbot", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sunilrufus/Emotion_support_chatbot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sunilrufus/Emotion_support_chatbot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sunilrufus/Emotion_support_chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sunilrufus/Emotion_support_chatbot

SGLang

How to use sunilrufus/Emotion_support_chatbot 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 "sunilrufus/Emotion_support_chatbot" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sunilrufus/Emotion_support_chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "sunilrufus/Emotion_support_chatbot" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sunilrufus/Emotion_support_chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

sunilrufus commited on May 27, 2024

Commit

1b74dbd

verified ·

1 Parent(s): dbbd7d6

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -6

README.md CHANGED Viewed

@@ -1,6 +1,12 @@
 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
@@ -17,13 +23,11 @@ tags: []
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
 - **Language(s) (NLP):** [More Information Needed]
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 ### Model Sources [optional]

 ---
 library_name: transformers
+tags:
+- emotion_support
+datasets:
+- ailover/ExTES
+language:
+- en
+pipeline_tag: text-generation
 ---
 # Model Card for Model ID
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** [Sunil Rufus]
+- **Model type:** [Text Generation]
 - **Language(s) (NLP):** [More Information Needed]
 - **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [Mistral Instruct v0.2]
 ### Model Sources [optional]