Instructions to use PrathamMMallya99/textilenet-qwen-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PrathamMMallya99/textilenet-qwen-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
model = PeftModel.from_pretrained(base_model, "PrathamMMallya99/textilenet-qwen-lora")

Transformers

How to use PrathamMMallya99/textilenet-qwen-lora with Transformers:

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

pipe = pipeline("text-generation", model="PrathamMMallya99/textilenet-qwen-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("PrathamMMallya99/textilenet-qwen-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PrathamMMallya99/textilenet-qwen-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PrathamMMallya99/textilenet-qwen-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PrathamMMallya99/textilenet-qwen-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PrathamMMallya99/textilenet-qwen-lora

SGLang

How to use PrathamMMallya99/textilenet-qwen-lora 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 "PrathamMMallya99/textilenet-qwen-lora" \
    --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": "PrathamMMallya99/textilenet-qwen-lora",
		"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 "PrathamMMallya99/textilenet-qwen-lora" \
        --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": "PrathamMMallya99/textilenet-qwen-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PrathamMMallya99/textilenet-qwen-lora with Docker Model Runner:
```
docker model run hf.co/PrathamMMallya99/textilenet-qwen-lora
```

Usage

To use this model, run the following code cell:

import torch
from peft import PeftModel
from transformers import Qwen2VLForConditionalGeneration

# 1. Load the Base Vision-Language Model
base_model = Qwen2VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2-VL-2B-Instruct",
    torch_dtype=torch.float16,  # Recommended to save memory
    device_map="auto"           # Automatically dispatches to GPU
)

# 2. Apply your trained LoRA adapter
model = PeftModel.from_pretrained(base_model, "PrathamMMallya99/textilenet-qwen-lora")
model.eval()

print("Successfully loaded VLM and LoRA adapter!")

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Model tree for PrathamMMallya99/textilenet-qwen-lora

Base model

Qwen/Qwen2-VL-2B

Finetuned

Qwen/Qwen2-VL-2B-Instruct

Adapter

(163)

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