Instructions to use hgupta2004/customer_support_llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hgupta2004/customer_support_llm with Transformers:

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

pipe = pipeline("text-generation", model="hgupta2004/customer_support_llm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hgupta2004/customer_support_llm")
model = AutoModelForCausalLM.from_pretrained("hgupta2004/customer_support_llm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hgupta2004/customer_support_llm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hgupta2004/customer_support_llm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hgupta2004/customer_support_llm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hgupta2004/customer_support_llm

SGLang

How to use hgupta2004/customer_support_llm 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 "hgupta2004/customer_support_llm" \
    --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": "hgupta2004/customer_support_llm",
		"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 "hgupta2004/customer_support_llm" \
        --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": "hgupta2004/customer_support_llm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hgupta2004/customer_support_llm with Docker Model Runner:
```
docker model run hf.co/hgupta2004/customer_support_llm
```

customer_support_llm / tokenizer.json

hgupta2004

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	{
	"version": "1.0",
	"truncation": null,
	"padding": null,
	"added_tokens": [
	{
	"id": 0,
	"content": "[PAD]",
	"single_word": false,
	"lstrip": false,
	"rstrip": false,
	"normalized": false,
	"special": true
	},
	{
	"id": 1,
	"content": "[BOS]",
	"single_word": false,
	"lstrip": false,
	"rstrip": false,
	"normalized": false,
	"special": true
	},
	{
	"id": 2,
	"content": "[EOS]",
	"single_word": false,
	"lstrip": false,
	"rstrip": false,
	"normalized": false,
	"special": true
	},
	{
	"id": 3,
	"content": "[UNK]",
	"single_word": false,
	"lstrip": false,
	"rstrip": false,
	"normalized": false,
	"special": true
	}
	],
	"normalizer": {
	"type": "Lowercase"
	},
	"pre_tokenizer": {
	"type": "Whitespace"
	},
	"post_processor": null,
	"decoder": null,
	"model": {
	"type": "WordLevel",
	"vocab": {
	"[PAD]": 0,
	"[BOS]": 1,
	"[EOS]": 2,
	"[UNK]": 3,
	"hello": 4,
	"customer": 5,
	"support": 6,
	"assistant": 7,
	"refund": 8,
	"billing": 9,
	"order": 10,
	"delay": 11,
	"issue": 12,
	"help": 13,
	"thanks": 14,
	"please": 15
	},
	"unk_token": "[UNK]"
	}
	}