Instructions to use shmobasher/Persian-VDU-Qwen2.5-7b-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shmobasher/Persian-VDU-Qwen2.5-7b-AWQ with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct-AWQ")
model = PeftModel.from_pretrained(base_model, "shmobasher/Persian-VDU-Qwen2.5-7b-AWQ")

Transformers

How to use shmobasher/Persian-VDU-Qwen2.5-7b-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="shmobasher/Persian-VDU-Qwen2.5-7b-AWQ")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("shmobasher/Persian-VDU-Qwen2.5-7b-AWQ", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shmobasher/Persian-VDU-Qwen2.5-7b-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shmobasher/Persian-VDU-Qwen2.5-7b-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shmobasher/Persian-VDU-Qwen2.5-7b-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shmobasher/Persian-VDU-Qwen2.5-7b-AWQ

SGLang

How to use shmobasher/Persian-VDU-Qwen2.5-7b-AWQ 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 "shmobasher/Persian-VDU-Qwen2.5-7b-AWQ" \
    --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": "shmobasher/Persian-VDU-Qwen2.5-7b-AWQ",
		"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 "shmobasher/Persian-VDU-Qwen2.5-7b-AWQ" \
        --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": "shmobasher/Persian-VDU-Qwen2.5-7b-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shmobasher/Persian-VDU-Qwen2.5-7b-AWQ with Docker Model Runner:
```
docker model run hf.co/shmobasher/Persian-VDU-Qwen2.5-7b-AWQ
```

shmobasher commited on Sep 25, 2025

Commit

5f22191

verified ·

1 Parent(s): b3a4305

Update adapter_config.json

Browse files

Files changed (1) hide show

adapter_config.json +1 -0

adapter_config.json CHANGED Viewed

@@ -2,6 +2,7 @@
   "alpha_pattern": {},
   "auto_mapping": null,
   "base_model_name_or_path": "Qwen/Qwen2.5-VL-7B-Instruct-AWQ",
   "bias": "none",
   "corda_config": null,
   "eva_config": null,

   "alpha_pattern": {},
   "auto_mapping": null,
   "base_model_name_or_path": "Qwen/Qwen2.5-VL-7B-Instruct-AWQ",
+  "model_type": "qwen2_vl",
   "bias": "none",
   "corda_config": null,
   "eva_config": null,