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

Libraries

How to use ChengLi0228/Angel-Observer with Transformers:

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

pipe = pipeline("text-generation", model="ChengLi0228/Angel-Observer")
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-Observer")
model = AutoModelForCausalLM.from_pretrained("ChengLi0228/Angel-Observer", 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-Observer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChengLi0228/Angel-Observer"
# 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-Observer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Angel-Observer

Angel-Observer reads a patient's presenting complaints and builds a directed symptom network (a Network Model): the thoughts, emotions, behaviors, bodily sensations and events in the case, and which ones set off which. It is stage 1 of Angel, a two-stage simulator of psychotherapy patients; Angel-Actor then role-plays the patient.

🎮 Live demo · 💻 Code: ANGEL-UserSim · 🎭 Angel-Actor

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

What it does

One checkpoint answers two prompts:

Step	Input	Output
S1: nodes	presenting complaints	`<think>…</think><GRAPH>{"symptoms": [...], "external_factors": [...]}</GRAPH>`
S2: edges	complaints + the S1 nodes	`<think>…</think><GRAPH>{"links": [{"from": ..., "to": ...}]}</GRAPH>`

In the Angel pipeline it also expands a short patient description into a detailed long profile for the Actor.

How to use

The system prompts the model was trained on are in the code repository, so the easiest way is through it:

git clone https://github.com/Scarelette/ANGEL-UserSim.git && cd ANGEL-UserSim
pip install -r model_training/observer/requirements-observer.txt

# one symptom network per case (JSONL with a "Complaints" field)
python -m model_training.observer.predict_network \
    --input data/examples/observer/case_reports.jsonl --output networks.jsonl

The model is downloaded from this page on first use. From Python:

from model_training.observer.predict_network import load_observer, predict_network

tokenizer, model = load_observer("ChengLi0228/Angel-Observer")
net = predict_network(tokenizer, model, "Alex, 29, reports three months of low mood, "
                      "poor sleep and withdrawing from friends after being laid off ...")
print(net["symptoms"])
print(net["graph"])   # [{"from": "being laid off", "to": "low mood"}, ...]

For the full Angel patient (Observer + Actor) see model_usage/. It uses the Qwen3 chat template, sampling with temperature 0.1 and top-p 0.9, and a bf16 model needs about 17 GB of GPU memory.

Training

Qwen3-8B, fine-tuned in four stages (QLoRA adapters on a 4-bit copy, each merged back in bf16):

Stage	Data	Settings
SFT, S1 (nodes)	5,599 rows	LoRA r=64 / α=16, lr 1e-4, 3 epochs
GRPO, S1	5,089 prompts, 600 steps	reward 0.4 · format + 0.6 · node recall (MiniLM cosine ≥ 0.75 against reference nodes)
SFT, S2 (edges)	2,138 rows	LoRA r=64 / α=16, lr 1e-4, 3 epochs
GRPO, S2	800 steps	reward 0.1 · format + 0.6 · edge precision + 0.3 · node coverage − size penalty; edges scored by a gpt-5-mini judge

The training data were built with GPT-5 from 510 of the 516 published psychotherapy case reports in PSYCHE, our graph-grounded dataset for psychological user simulation (release coming soon). The case reports are copyrighted and are not redistributed. Full recipe: model_training/observer/.

Intended use and limitations

Research use in user simulation, for example generating varied, structured patient cases to train or evaluate therapy-support systems and clinicians-in-training.
Not a diagnostic or clinical tool. The networks are the model's reading of a text, not clinical assessments, and they can be wrong, incomplete or reflect biases in the source case reports.
Trained on English case-report language; other languages and very different writing styles are untested.
Case material can involve self-harm, suicidality, trauma and substance use, so the model's outputs can too.

Citation

Coming soon.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for ChengLi0228/Angel-Observer

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

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