Instructions to use rty813/Qwen2.5-0.5B-Instruct-oldprobe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rty813/Qwen2.5-0.5B-Instruct-oldprobe with Transformers:

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

pipe = pipeline("text-generation", model="rty813/Qwen2.5-0.5B-Instruct-oldprobe")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rty813/Qwen2.5-0.5B-Instruct-oldprobe")
model = AutoModelForCausalLM.from_pretrained("rty813/Qwen2.5-0.5B-Instruct-oldprobe", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rty813/Qwen2.5-0.5B-Instruct-oldprobe with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rty813/Qwen2.5-0.5B-Instruct-oldprobe"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rty813/Qwen2.5-0.5B-Instruct-oldprobe",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rty813/Qwen2.5-0.5B-Instruct-oldprobe

SGLang

How to use rty813/Qwen2.5-0.5B-Instruct-oldprobe 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 "rty813/Qwen2.5-0.5B-Instruct-oldprobe" \
    --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": "rty813/Qwen2.5-0.5B-Instruct-oldprobe",
		"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 "rty813/Qwen2.5-0.5B-Instruct-oldprobe" \
        --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": "rty813/Qwen2.5-0.5B-Instruct-oldprobe",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rty813/Qwen2.5-0.5B-Instruct-oldprobe with Docker Model Runner:
```
docker model run hf.co/rty813/Qwen2.5-0.5B-Instruct-oldprobe
```

Qwen2.5-0.5B-Instruct-oldprobe (bf16)

Full-precision (bf16) PyTorch port of Qwen/Qwen2.5-0.5B-Instruct, for vLLM / TGI / Xinference / transformers users who don't want the GGUF quant.

Format: pytorch_model.bin (bf16). safetensors conversion in progress.
Architecture: identical to the original — drop-in for AutoModelForCausalLM.from_pretrained("rty813/Qwen2.5-0.5B-Instruct-oldprobe").
Also loads in vLLM (vllm serve rty813/Qwen2.5-0.5B-Instruct-oldprobe) and Xinference (search "Qwen2.5-0.5B-Instruct").

Original model card:

license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-0.5B tags: - chat library_name: transformers

Qwen2.5-0.5B-Instruct

Introduction

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:

Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
Long-context Support up to 128K tokens and can generate up to 8K tokens.
Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

This repo contains the instruction-tuned 0.5B Qwen2.5 model, which has the following features:

Type: Causal Language Models
Training Stage: Pretraining & Post-training
Architecture: transformers with RoPE

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Model tree for rty813/Qwen2.5-0.5B-Instruct-oldprobe

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

(1104)

this model