Instructions to use mudasir13cs/Field-adaptive-description-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mudasir13cs/Field-adaptive-description-generator with Transformers:

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

pipe = pipeline("text-generation", model="mudasir13cs/Field-adaptive-description-generator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mudasir13cs/Field-adaptive-description-generator", dtype="auto")

PEFT
How to use mudasir13cs/Field-adaptive-description-generator with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mudasir13cs/Field-adaptive-description-generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mudasir13cs/Field-adaptive-description-generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mudasir13cs/Field-adaptive-description-generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mudasir13cs/Field-adaptive-description-generator

SGLang

How to use mudasir13cs/Field-adaptive-description-generator 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 "mudasir13cs/Field-adaptive-description-generator" \
    --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": "mudasir13cs/Field-adaptive-description-generator",
		"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 "mudasir13cs/Field-adaptive-description-generator" \
        --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": "mudasir13cs/Field-adaptive-description-generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mudasir13cs/Field-adaptive-description-generator with Docker Model Runner:
```
docker model run hf.co/mudasir13cs/Field-adaptive-description-generator
```

mudasir13cs commited on Nov 12, 2025

Commit

90944fa

verified ·

1 Parent(s): c2703e9

Update README.md

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@@ -78,8 +78,24 @@ model = AutoModelForCausalLM.from_pretrained("mudasir13cs/Field-adaptive-descrip
 tokenizer = AutoTokenizer.from_pretrained("mudasir13cs/Field-adaptive-description-generator")
 # Generate content
-input_text = "Generate 8 different search queries for: Title: Business Strategy Template, Description: Professional business strategy presentation, Industries: Business, Categories: Strategy, Tags: business, strategy, planning"
 inputs = tokenizer(input_text, return_tensors="pt")
 outputs = model.generate(**inputs, max_length=512, temperature=0.7, do_sample=True)
 generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

 tokenizer = AutoTokenizer.from_pretrained("mudasir13cs/Field-adaptive-description-generator")
 # Generate content
+input_text = """<start_of_turn>user
+Generate a 50-80 word SEO-friendly description for this presentation template:
+    Title: Modern Business Presentation
+    Visual Elements: minimalist design, blue gradient background, geometric shapes
+    Industries: Business, Marketing
+    Categories: Corporate, Professional
+    Tags: Modern, Clean, Professional
+    Requirements:
+        - Describe visual style naturally
+        - Mention 2-3 specific use cases
+        - Integrate keywords organically (no markdown/bold formatting)
+        - Professional yet engaging tone
+        - Exactly 50-80 words
+        - Start directly with the description (no prefixes)
+<end_of_turn>
+<start_of_turn>model
+"""
 inputs = tokenizer(input_text, return_tensors="pt")
 outputs = model.generate(**inputs, max_length=512, temperature=0.7, do_sample=True)
 generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)