Text Generation
Transformers
Safetensors
Chinese
English
qwen3
mental-health
psychological-counseling
chinese
long-context
dialogue
campus-counseling
conversational
text-generation-inference
Instructions to use EdwinUstb/CPCD-Chat-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdwinUstb/CPCD-Chat-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EdwinUstb/CPCD-Chat-4B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EdwinUstb/CPCD-Chat-4B") model = AutoModelForCausalLM.from_pretrained("EdwinUstb/CPCD-Chat-4B") 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
- vLLM
How to use EdwinUstb/CPCD-Chat-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EdwinUstb/CPCD-Chat-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EdwinUstb/CPCD-Chat-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/EdwinUstb/CPCD-Chat-4B
- SGLang
How to use EdwinUstb/CPCD-Chat-4B 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 "EdwinUstb/CPCD-Chat-4B" \ --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": "EdwinUstb/CPCD-Chat-4B", "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 "EdwinUstb/CPCD-Chat-4B" \ --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": "EdwinUstb/CPCD-Chat-4B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use EdwinUstb/CPCD-Chat-4B with Docker Model Runner:
docker model run hf.co/EdwinUstb/CPCD-Chat-4B
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license: apache-2.0
language:
- zh
- en
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen3-4B-Base
tags:
- qwen3
- mental-health
- psychological-counseling
- chinese
- long-context
- dialogue
- campus-counseling
---
# CPCD-Chat-8B
CPCD-Chat-8B is a Chinese long-horizon campus psychological counseling dialogue model developed as part of the **Psy-Chronicle** project.
- **Model page**: https://huggingface.co/EdwinUstb/CPCD-Chat-4B
- **Project GitHub**: https://github.com/EdwinUSTB/Psy-Chronicle
- **The Hugging Face paper URL**: https://huggingface.co/papers/2605.22140
- **The arXiv URL**: https://arxiv.org/abs/2605.22140
## Model Description
CPCD-Chat-8B is fine-tuned from **Qwen3-4B-Base** on CPCD, a synthetic Chinese long-horizon campus psychological counseling dialogue dataset.
The model is designed for research on:
- long-horizon psychological counseling dialogue generation;
- campus mental-health support scenarios;
- cross-session counseling memory;
- student stress-event evolution;
- temporal-causal reasoning in counseling conversations.
## Dataset
The model is trained on **CPCD**, a Chinese long-horizon dialogue dataset for college psychological counseling scenarios.
CPCD is generated by the Psy-Chronicle framework, which constructs:
1. structured student profiles;
2. semester-level temporal stress event graphs;
3. cross-session counseling dialogues;
4. structured memory summaries.
Dataset statistics:
| Component | Value |
|---|---:|
| Student profiles | 100 |
| Counseling dialogue units | 90,000 |
| Chinese characters | ~11.45M |
| Scenario | Chinese campus psychological counseling |
## Psy-Chronicle Framework
Psy-Chronicle synthesizes long-horizon counseling trajectories through a structured pipeline:
```text
Student Profile
↓
Temporal Stress Event Graph
↓
Cross-session Counseling Simulation
↓
Structured Memory Update
↓
CPCD Dataset / CPCD-Bench
```
Unlike single-turn or short multi-turn counseling datasets, Psy-Chronicle focuses on how college students' psychological distress accumulates, interacts, and evolves across a semester.
## CPCD-Bench
CPCD-Bench evaluates long-horizon campus counseling capabilities from three dimensions:
| Task | Description |
|---|---|
| Session-level Response | Generate appropriate counselor responses using current context and historical memory |
| Memory Recall | Recall factual information from long counseling histories |
| Temporal-Causal Reasoning | Analyze chronological event development and causal relationships |
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "EdwinUstb/CPCD-Chat-4B"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
messages = [
{
"role": "user",
"content": "我最近因为学业和家庭压力感到很焦虑,不知道该怎么办。"
}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.7,
top_p=0.9
)
response = tokenizer.decode(
outputs[0][inputs.input_ids.shape[-1]:],
skip_special_tokens=True
)
print(response)
```
## Intended Use
This model is intended for research on:
- psychological counseling dialogue modeling;
- long-horizon dialogue generation;
- cross-session memory modeling;
- campus mental-health support datasets;
- temporal-causal reasoning in counseling scenarios.
## Limitations
CPCD-Chat-8B is trained on synthetic counseling data. It may generate responses that are incomplete, overly generic, or inappropriate in high-risk mental-health situations.
The model should **not** be used as a substitute for professional psychological counseling, clinical diagnosis, or treatment.
## Ethical Considerations
This model is released for research and evaluation purposes only.
Users should be aware that:
- the training data are synthetic and do not represent real counseling records;
- the model may fail to detect or properly handle crisis situations;
- any deployment-oriented use should include professional review, safety monitoring, and clear user-facing disclaimers.
## Citation
If you find this model or project useful, please cite:
```bibtex
@misc{gou2026psychronicle,
title = {Psy-Chronicle: A Structured Pipeline for Synthesizing Long-Horizon Campus Psychological Counseling Dialogues},
author = {Chaogui Gou and Jiarui Liang},
year = {2026},
note = {Preprint},
url = {https://github.com/EdwinUSTB/Psy-Chronicle}
}
```
## License
This model is released under the Apache License 2.0.
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