Instructions to use nagham-mlb/supportbot-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nagham-mlb/supportbot-model with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nagham-mlb/supportbot-model")
model = AutoModelForCausalLM.from_pretrained("nagham-mlb/supportbot-model")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nagham-mlb/supportbot-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nagham-mlb/supportbot-model

SGLang

How to use nagham-mlb/supportbot-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 "nagham-mlb/supportbot-model" \
    --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": "nagham-mlb/supportbot-model",
		"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 "nagham-mlb/supportbot-model" \
        --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": "nagham-mlb/supportbot-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nagham-mlb/supportbot-model with Docker Model Runner:
```
docker model run hf.co/nagham-mlb/supportbot-model
```

SupportBot Customer Support Model

This model is a fine-tuned version of distilgpt2 specifically designed for customer support conversations on the SupportBot platform.

Model Description

A conversational AI model that provides helpful, accurate responses to common customer support queries including password resets, order tracking, return policies, account management, and troubleshooting.

Intended Uses

This model is designed to:

Answer customer support questions automatically
Provide consistent, accurate responses
Reduce human agent workload
Offer 24/7 support availability

How to Use

Python (Transformers)

from transformers import pipeline

generator = pipeline('text-generation', model='nagham-mlb/supportbot-model')

prompt = "User: How do I reset my password?\nBot:"
response = generator(prompt, max_new_tokens=100, temperature=0.7)
print(response[0]['generated_text'])

Downloads last month: 5

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for nagham-mlb/supportbot-model

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distilbert/distilgpt2

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