Instructions to use doupari/Qwen2.5-0.5B-Instruct-think-0701 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use doupari/Qwen2.5-0.5B-Instruct-think-0701 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "doupari/Qwen2.5-0.5B-Instruct-think-0701")

Transformers

How to use doupari/Qwen2.5-0.5B-Instruct-think-0701 with Transformers:

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

pipe = pipeline("text-generation", model="doupari/Qwen2.5-0.5B-Instruct-think-0701")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("doupari/Qwen2.5-0.5B-Instruct-think-0701", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use doupari/Qwen2.5-0.5B-Instruct-think-0701 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "doupari/Qwen2.5-0.5B-Instruct-think-0701"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "doupari/Qwen2.5-0.5B-Instruct-think-0701",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/doupari/Qwen2.5-0.5B-Instruct-think-0701

SGLang

How to use doupari/Qwen2.5-0.5B-Instruct-think-0701 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 "doupari/Qwen2.5-0.5B-Instruct-think-0701" \
    --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": "doupari/Qwen2.5-0.5B-Instruct-think-0701",
		"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 "doupari/Qwen2.5-0.5B-Instruct-think-0701" \
        --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": "doupari/Qwen2.5-0.5B-Instruct-think-0701",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use doupari/Qwen2.5-0.5B-Instruct-think-0701 with Docker Model Runner:
```
docker model run hf.co/doupari/Qwen2.5-0.5B-Instruct-think-0701
```

Qwen2.5-0.5B-Instruct-think-0701

LoRA adapter fine-tuned from Qwen/Qwen2.5-0.5B-Instruct for the graph QA think extraction setting.

Checkpoint selection

Uploaded checkpoint: checkpoint-2094

This checkpoint was selected from:

/home/hyeseojeon/data/graph/outputs/finetune/Qwen2.5-0.5B-Instruct-think

Selection criterion: lowest eval loss found in trainer_state.json.

Best eval loss: 0.09406370669603348

Adapter config

PEFT type: LoRA
Rank: 16
LoRA alpha: 32
LoRA dropout: 0.05
Base model: Qwen/Qwen2.5-0.5B-Instruct

Downloads last month: 25

Model tree for doupari/Qwen2.5-0.5B-Instruct-think-0701

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Adapter

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this model