Instructions to use Vira21/Qwen2.5-1.5B-Instruct-QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Vira21/Qwen2.5-1.5B-Instruct-QA with PEFT:
```
Task type is invalid.
```

How to use Vira21/Qwen2.5-1.5B-Instruct-QA with Transformers:

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

pipe = pipeline("text-generation", model="Vira21/Qwen2.5-1.5B-Instruct-QA")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Vira21/Qwen2.5-1.5B-Instruct-QA", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vira21/Qwen2.5-1.5B-Instruct-QA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vira21/Qwen2.5-1.5B-Instruct-QA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vira21/Qwen2.5-1.5B-Instruct-QA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Vira21/Qwen2.5-1.5B-Instruct-QA

SGLang

How to use Vira21/Qwen2.5-1.5B-Instruct-QA 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 "Vira21/Qwen2.5-1.5B-Instruct-QA" \
    --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": "Vira21/Qwen2.5-1.5B-Instruct-QA",
		"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 "Vira21/Qwen2.5-1.5B-Instruct-QA" \
        --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": "Vira21/Qwen2.5-1.5B-Instruct-QA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Vira21/Qwen2.5-1.5B-Instruct-QA with Docker Model Runner:
```
docker model run hf.co/Vira21/Qwen2.5-1.5B-Instruct-QA
```

Qwen2.5-1.5B-Instruct-QA

Commit History

Upload all files, checkpoints + runs via LFS

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Vira Rithy commited on Sep 7, 2025

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Track tokenizer.json with LFS

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Vira Rithy commited on Sep 7, 2025

Upload checkpoints and training logs (runs)

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Vira Rithy commited on Sep 7, 2025

Upload checkpoints and training logs (runs)

cd03aec

Vira Rithy commited on Sep 7, 2025

Upload checkpoints and training logs (runs)

1b89053

Vira Rithy commited on Sep 7, 2025

initial commit

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unverified

Vira21 commited on Sep 7, 2025

Commit History

Upload all files, checkpoints + runs via LFS f21e178

Upload all checkpoints + runs via LFS 46923df

Track tokenizer.json with LFS 2d591f2

Upload checkpoints and training logs (runs) 36b68ca

Upload checkpoints and training logs (runs) cd03aec

Upload checkpoints and training logs (runs) 1b89053

initial commit ba25e89 unverified

Upload all files, checkpoints + runs via LFS

f21e178

Upload all checkpoints + runs via LFS

46923df

Track tokenizer.json with LFS

2d591f2

Upload checkpoints and training logs (runs)

36b68ca

Upload checkpoints and training logs (runs)

cd03aec

Upload checkpoints and training logs (runs)

1b89053

initial commit

ba25e89
unverified