Instructions to use LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-VL-2B-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr")

Transformers

How to use LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr with Transformers:

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

pipe = pipeline("text-generation", model="LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr

SGLang

How to use LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr 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 "LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr" \
    --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": "LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr",
		"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 "LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr" \
        --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": "LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr with Docker Model Runner:
```
docker model run hf.co/LINGESH-7/qwen2_v1_2b-FT-on-latex_ocr
```

qwen2_v1_2b-FT-on-latex_ocr / adapter_config.json

LINGESH-7

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	{
	"alora_invocation_tokens": null,
	"alpha_pattern": {},
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	"auto_mapping": {
	"base_model_class": "Qwen2VLForConditionalGeneration",
	"parent_library": "transformers.models.qwen2_vl.modeling_qwen2_vl",
	"unsloth_fixed": true
	},
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	"target_parameters": null,
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