Instructions to use ChengLi0228/Angel-Actor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChengLi0228/Angel-Actor with Transformers:

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

pipe = pipeline("text-generation", model="ChengLi0228/Angel-Actor")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("ChengLi0228/Angel-Actor")
model = AutoModelForCausalLM.from_pretrained("ChengLi0228/Angel-Actor", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ChengLi0228/Angel-Actor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChengLi0228/Angel-Actor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChengLi0228/Angel-Actor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ChengLi0228/Angel-Actor

SGLang

How to use ChengLi0228/Angel-Actor 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 "ChengLi0228/Angel-Actor" \
    --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": "ChengLi0228/Angel-Actor",
		"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 "ChengLi0228/Angel-Actor" \
        --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": "ChengLi0228/Angel-Actor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ChengLi0228/Angel-Actor with Docker Model Runner:
```
docker model run hf.co/ChengLi0228/Angel-Actor
```

Angel-Actor / README.md

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---
license: apache-2.0
base_model: Qwen/Qwen3-8B
base_model_relation: finetune
library_name: transformers
pipeline_tag: text-generation
language:
- en
tags:
- mental-health
- user-simulation
- patient-simulation
- psychotherapy
- role-play
- dpo
- qwen3
---
# Angel-Actor

**Angel-Actor** role-plays a **psychotherapy patient** across a multi-turn
conversation. You are the therapist; it answers in character, keeps some
things back, and opens up gradually. It is stage 2 of **Angel**, a two-stage
simulator of psychotherapy patients:
[Angel-Observer](https://huggingface.co/ChengLi0228/Angel-Observer) first
turns a short patient description into a detailed profile and symptom
network, and the Actor plays that patient.

🎮 **[Live demo: talk to a patient](https://eval.angel-simulation.org/demo)** · 💻 **[Code: ANGEL-UserSim](https://github.com/Scarelette/ANGEL-UserSim)** · 🕸️ **[Angel-Observer](https://huggingface.co/ChengLi0228/Angel-Observer)**

```
short description ──► Angel-Observer ──► long profile + symptom network ──► Angel-Actor ──► patient replies
```

## How to use

The quickest way is the [live demo](https://eval.angel-simulation.org/demo).
To run it yourself, use the code repository, which builds the patient prompt
from a profile and downloads both models from Hugging Face on first use:

```bash
git clone https://github.com/Scarelette/ANGEL-UserSim.git && cd ANGEL-UserSim
pip install -r model_usage/requirements-usage.txt
python -m model_usage.angel chat --short-profile-file model_usage/examples/example_short_profile.txt
```

From Python:

```python
from model_usage.angel import AngelModel
with AngelModel() as model:
    note = open("model_usage/examples/example_short_profile.txt").read()
    print(model.send("me", "Hi, what brings you in today?", short_profile=note)["reply"])
    print(model.send("me", "How long has that been going on?")["reply"])
```

Both models together need a GPU with about 40 GB. Details:
[`model_usage/`](https://github.com/Scarelette/ANGEL-UserSim/tree/main/model_usage).

**Output format.** The patient's description goes in the system prompt, and
therapist turns are `user` turns. Each reply has two parts:
`<state>…</state>` is the patient's inner state (hidden from the therapist)
and `<word>…</word>` is what the patient says. `model_usage` shows only the
`<word>` part. Use the Qwen3 chat template with thinking off
(`enable_thinking=False`).

## Training

Qwen3-8B, trained in two stages on synthetic therapy conversations:

| Stage | Data | Settings |
|---|---|---|
| SFT (QLoRA) | 423 conversations between a prompted Qwen3-30B-A3B patient and an LLM therapist | LoRA r=64 / α=16, lr 1e-4, 5 epochs, loss on patient turns |
| DPO | 6,901 preference pairs: at each turn the SFT model sampled up to 5 replies and a Claude judge picked the best and worst | LoRA r=16 / α=32, lr 2e-6, β 0.1, 2 epochs; reference = the SFT model |

Each conversation followed a symptom network from Angel-Observer in which
part of the network was **masked**: those mechanisms start out unrecognised
by the patient and surface as the conversation goes on. The networks come
from **PSYCHE**, our graph-grounded dataset for psychological user
simulation (release coming soon). The judge scored replies on safety,
structure, specificity, state alignment, consistency with the history,
progress and naturalness. Full recipe:
[`model_training/actor/`](https://github.com/Scarelette/ANGEL-UserSim/tree/main/model_training/actor).

## Intended use and limitations

- **Research use**: simulated patients for training and evaluating
  therapy-support systems, and for practice by clinicians-in-training.
- **Not therapy, and not a real person.** It is a simulation and can break
  character, contradict its profile or behave unlike a real patient. Asking
  it to summarise the session, give advice or plan the next session tends to
  pull it out of role.
- **Sensitive content.** By design it portrays depression, anxiety, trauma,
  substance use and passive suicidal thoughts. Its training prompts keep
  suicidality passive and vague (no intent, plans or methods), but outputs
  are not guaranteed to follow that. Use appropriate safeguards and don't
  expose it to vulnerable users as if it were a support tool.
- English only.

## Citation

Coming soon.

	---
	license: apache-2.0
	base_model: Qwen/Qwen3-8B
	base_model_relation: finetune
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- mental-health
	- user-simulation
	- patient-simulation
	- psychotherapy
	- role-play
	- dpo
	- qwen3
	---

	# Angel-Actor

	Angel-Actor role-plays a psychotherapy patient across a multi-turn
	conversation. You are the therapist; it answers in character, keeps some
	things back, and opens up gradually. It is stage 2 of Angel, a two-stage
	simulator of psychotherapy patients:
	[Angel-Observer](https://huggingface.co/ChengLi0228/Angel-Observer) first
	turns a short patient description into a detailed profile and symptom
	network, and the Actor plays that patient.

	🎮 [Live demo: talk to a patient](https://eval.angel-simulation.org/demo) · 💻 [Code: ANGEL-UserSim](https://github.com/Scarelette/ANGEL-UserSim) · 🕸️ [Angel-Observer](https://huggingface.co/ChengLi0228/Angel-Observer)

	```
	short description ──► Angel-Observer ──► long profile + symptom network ──► Angel-Actor ──► patient replies
	```

	## How to use

	The quickest way is the [live demo](https://eval.angel-simulation.org/demo).
	To run it yourself, use the code repository, which builds the patient prompt
	from a profile and downloads both models from Hugging Face on first use:

	```bash
	git clone https://github.com/Scarelette/ANGEL-UserSim.git && cd ANGEL-UserSim
	pip install -r model_usage/requirements-usage.txt
	python -m model_usage.angel chat --short-profile-file model_usage/examples/example_short_profile.txt
	```

	From Python:

	```python
	from model_usage.angel import AngelModel

	with AngelModel() as model:
	note = open("model_usage/examples/example_short_profile.txt").read()
	print(model.send("me", "Hi, what brings you in today?", short_profile=note)["reply"])
	print(model.send("me", "How long has that been going on?")["reply"])
	```

	Both models together need a GPU with about 40 GB. Details:
	[`model_usage/`](https://github.com/Scarelette/ANGEL-UserSim/tree/main/model_usage).

	Output format. The patient's description goes in the system prompt, and
	therapist turns are `user` turns. Each reply has two parts:
	`<state>…</state>` is the patient's inner state (hidden from the therapist)
	and `<word>…</word>` is what the patient says. `model_usage` shows only the
	`<word>` part. Use the Qwen3 chat template with thinking off
	(`enable_thinking=False`).

	## Training

	Qwen3-8B, trained in two stages on synthetic therapy conversations:

	\| Stage \| Data \| Settings \|
	\|---\|---\|---\|
	\| SFT (QLoRA) \| 423 conversations between a prompted Qwen3-30B-A3B patient and an LLM therapist \| LoRA r=64 / α=16, lr 1e-4, 5 epochs, loss on patient turns \|
	\| DPO \| 6,901 preference pairs: at each turn the SFT model sampled up to 5 replies and a Claude judge picked the best and worst \| LoRA r=16 / α=32, lr 2e-6, β 0.1, 2 epochs; reference = the SFT model \|

	Each conversation followed a symptom network from Angel-Observer in which
	part of the network was masked: those mechanisms start out unrecognised
	by the patient and surface as the conversation goes on. The networks come
	from PSYCHE, our graph-grounded dataset for psychological user
	simulation (release coming soon). The judge scored replies on safety,
	structure, specificity, state alignment, consistency with the history,
	progress and naturalness. Full recipe:
	[`model_training/actor/`](https://github.com/Scarelette/ANGEL-UserSim/tree/main/model_training/actor).

	## Intended use and limitations

	- Research use: simulated patients for training and evaluating
	therapy-support systems, and for practice by clinicians-in-training.
	- Not therapy, and not a real person. It is a simulation and can break
	character, contradict its profile or behave unlike a real patient. Asking
	it to summarise the session, give advice or plan the next session tends to
	pull it out of role.
	- Sensitive content. By design it portrays depression, anxiety, trauma,
	substance use and passive suicidal thoughts. Its training prompts keep
	suicidality passive and vague (no intent, plans or methods), but outputs
	are not guaranteed to follow that. Use appropriate safeguards and don't
	expose it to vulnerable users as if it were a support tool.
	- English only.

	## Citation

	Coming soon.