Instructions to use Yukyin/deepsupport-warm-lora-oss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yukyin/deepsupport-warm-lora-oss with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-32B-Instruct")
model = PeftModel.from_pretrained(base_model, "Yukyin/deepsupport-warm-lora-oss")

Transformers

How to use Yukyin/deepsupport-warm-lora-oss with Transformers:

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

pipe = pipeline("text-generation", model="Yukyin/deepsupport-warm-lora-oss")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Yukyin/deepsupport-warm-lora-oss", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yukyin/deepsupport-warm-lora-oss with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Yukyin/deepsupport-warm-lora-oss

SGLang

How to use Yukyin/deepsupport-warm-lora-oss 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 "Yukyin/deepsupport-warm-lora-oss" \
    --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": "Yukyin/deepsupport-warm-lora-oss",
		"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 "Yukyin/deepsupport-warm-lora-oss" \
        --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": "Yukyin/deepsupport-warm-lora-oss",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Yukyin/deepsupport-warm-lora-oss with Docker Model Runner:
```
docker model run hf.co/Yukyin/deepsupport-warm-lora-oss
```

deepsupport-warm-lora-oss / README.md

Yukyin

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	---
	language: en
	license: other
	library_name: peft
	base_model: Qwen/Qwen2.5-32B-Instruct
	pipeline_tag: text-generation
	tags:
	- lora
	- peft
	- transformers
	- qwen2.5
	- conversational
	---

	# DeepSupport Warm ❤️‍🩹 - LoRA adapter
	This repository provides a LoRA adapter for DeepSupport Warm, an emotional-holding companion that offers gentle reflection and warm support without rushing into what to do next.

	## What it does ✨
	DeepSupport Warm is designed to help users feel held and less alone in the moment:

	- Validate and name feelings without judging
	- Stay with emotion first before problem-solving
	- Offer gentle grounding and a small next step only if you want



	## Quick start 🚀
	### 1) Install
	```bash
	pip install transformers peft accelerate torch
	```

	### 2) Load base model and LoRA adapter
	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from peft import PeftModel

	base_id = "Qwen/Qwen2.5-32B-Instruct" # Base model we used. You may replace it with another compatible base model
	lora_id = "Yukyin/deepsupport-warm-lora-oss"

	tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
	base = AutoModelForCausalLM.from_pretrained(
	base_id,
	torch_dtype=torch.float16,
	device_map="auto",
	trust_remote_code=True,
	)

	model = PeftModel.from_pretrained(base, lora_id)
	model.eval()

	messages = [
	{"role": "user", "content": "我最近压力很大，感觉自己一直在被否定。"},
	]

	inputs = tokenizer.apply_chat_template(
	messages,
	tokenize=True,
	add_generation_prompt=True,
	return_tensors="pt",
	).to(model.device)

	with torch.no_grad():
	out = model.generate(
	inputs,
	max_new_tokens=256,
	do_sample=True,
	temperature=0.85,
	top_p=0.9,
	repetition_penalty=1.12,
	no_repeat_ngram_size=4,
	)

	print(tokenizer.decode(out[0], skip_special_tokens=True))
	```


	## Training data and release notes 📊
	- This OSS LoRA adapter is trained on de-identified versions of the original data.
	- The original internal LoRA adapter was trained on non-de-identified data and cannot be open-sourced at this time.
	- More details and examples are provided in the [GitHub repo](https://github.com/Yukyin/DeepSupport).


	## Safety and privacy ⚠️
	This project is intended for supportive conversation only.
	It does not provide professional advice, diagnosis, or therapy. Please seek qualified professional help when needed.


	## License 📜
	This adapter is released for noncommercial use. See the [GitHub repo](https://github.com/Yukyin/DeepSupport) for the full license text and commercial licensing terms.


	## Citation 📚
	```bibtex
	@software{deepsupport_warm_2026,
	author = {Yuyan Chen},
	title = {DeepSupport Warm: An emotional-holding companion for supportive dialogue},
	year = {2026},
	version = {oss},
	url = {\url{https://github.com/Yukyin/DeepSupport/DeepSupport_Warm}}
	}
	```

	## Links
	- GitHub: https://github.com/Yukyin/DeepSupport
	- LoRA adapter: https://huggingface.co/Yukyin/deepsupport-warm-lora-oss