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

Libraries

How to use seo/Support with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("seo/Support")
model = AutoModelForCausalLM.from_pretrained("seo/Support")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use seo/Support with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/seo/Support

SGLang

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

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

seo commited on Aug 23, 2025

Commit

cbcaa50

verified ·

1 Parent(s): f76cd53

Training in progress, epoch 1

Browse files

Files changed (4) hide show

README.md +1 -2
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runs/Aug23_22-57-46_M-HJ.local/events.out.tfevents.1755957471.M-HJ.local.67129.1 +3 -0
training_args.bin +1 -1

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@@ -1,5 +1,4 @@
 ---
-base_model: google/gemma-3-270m-it
 library_name: transformers
 model_name: Support
 tags:
@@ -11,7 +10,7 @@ licence: license
 # Model Card for Support
-This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start

 ---
 library_name: transformers
 model_name: Support
 tags:
 # Model Card for Support
+This model is a fine-tuned version of [None](https://huggingface.co/None).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start

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