Instructions to use Shiyunee/Honest-Qwen2.5-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shiyunee/Honest-Qwen2.5-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Shiyunee/Honest-Qwen2.5-7B-Instruct")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Shiyunee/Honest-Qwen2.5-7B-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Shiyunee/Honest-Qwen2.5-7B-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Shiyunee/Honest-Qwen2.5-7B-Instruct

SGLang

How to use Shiyunee/Honest-Qwen2.5-7B-Instruct 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 "Shiyunee/Honest-Qwen2.5-7B-Instruct" \
    --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": "Shiyunee/Honest-Qwen2.5-7B-Instruct",
		"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 "Shiyunee/Honest-Qwen2.5-7B-Instruct" \
        --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": "Shiyunee/Honest-Qwen2.5-7B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Shiyunee/Honest-Qwen2.5-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/Shiyunee/Honest-Qwen2.5-7B-Instruct
```

Add pipeline tag, library name, and prominent GitHub link

by nielsr HF Staff - opened Oct 26, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+15

-8

nielsr

Oct 26, 2025

This PR enhances the model card by adding key metadata and improving content organization:

pipeline_tag: text-generation: This helps users discover the model when filtering by text generation models on the Hub, reflecting its function in confidence estimation for LLM outputs.
library_name: transformers: This enables the automated "how to use" code snippet on the Hub, as the model's code clearly leverages the 🤗 Transformers library (e.g., AutoModel.from_pretrained, PeftModel.from_pretrained).
A prominent link to the GitHub repository is added at the top of the README for easier access to the associated code and project details.

These additions will make the model more accessible and easier for users to integrate into their workflows.

Add pipeline tag, library name, and prominent GitHub link1a58f8cb

Shiyunee changed pull request status to merged Oct 26, 2025

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