Instructions to use mzhaoshuai/zephyr-7b-alpha-conf-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mzhaoshuai/zephyr-7b-alpha-conf-sft with Transformers:

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

pipe = pipeline("text-generation", model="mzhaoshuai/zephyr-7b-alpha-conf-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mzhaoshuai/zephyr-7b-alpha-conf-sft")
model = AutoModelForCausalLM.from_pretrained("mzhaoshuai/zephyr-7b-alpha-conf-sft", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mzhaoshuai/zephyr-7b-alpha-conf-sft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mzhaoshuai/zephyr-7b-alpha-conf-sft

SGLang

How to use mzhaoshuai/zephyr-7b-alpha-conf-sft 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 "mzhaoshuai/zephyr-7b-alpha-conf-sft" \
    --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": "mzhaoshuai/zephyr-7b-alpha-conf-sft",
		"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 "mzhaoshuai/zephyr-7b-alpha-conf-sft" \
        --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": "mzhaoshuai/zephyr-7b-alpha-conf-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mzhaoshuai/zephyr-7b-alpha-conf-sft with Docker Model Runner:
```
docker model run hf.co/mzhaoshuai/zephyr-7b-alpha-conf-sft
```

Improve model card: Add pipeline tag, update library_name, and link paper/code/abstract

by nielsr HF Staff - opened Oct 14, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+22

-6

nielsr

Oct 14, 2025

This PR significantly enhances the model card for mzhaoshuai/zephyr-7b-alpha-conf-sft by:

Adding the pipeline_tag: text-generation to ensure better discoverability and correct categorization on the Hugging Face Hub.
Updating library_name from peft to transformers. This is because the model is a PEFT adapter built on a transformers-compatible base model (MistralForCausalLM), and transformers is the primary library used for loading and inference. This change enables the automated inference widget on the Hub.
Adding a link to the associated paper: Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data.
Adding a link to the GitHub repository: https://github.com/mzhaoshuai/RefAlign.
Including the full abstract of the paper to provide a detailed and comprehensive description of the RefAlign framework and this model's role within it.
Adding the BibTeX citation for proper referencing.

These updates aim to make the model card more informative, discoverable, and user-friendly for the community.

Improve model card: Add pipeline tag, update library_name, and link paper/code/abstract2843cf0b

mzhaoshuai changed pull request status to merged Oct 16, 2025

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