Instructions to use jun10k/Qwen1.5-7B-MeChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jun10k/Qwen1.5-7B-MeChat with Transformers:

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

pipe = pipeline("text-generation", model="jun10k/Qwen1.5-7B-MeChat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jun10k/Qwen1.5-7B-MeChat")
model = AutoModelForCausalLM.from_pretrained("jun10k/Qwen1.5-7B-MeChat")
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 jun10k/Qwen1.5-7B-MeChat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jun10k/Qwen1.5-7B-MeChat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jun10k/Qwen1.5-7B-MeChat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jun10k/Qwen1.5-7B-MeChat

SGLang

How to use jun10k/Qwen1.5-7B-MeChat 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 "jun10k/Qwen1.5-7B-MeChat" \
    --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": "jun10k/Qwen1.5-7B-MeChat",
		"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 "jun10k/Qwen1.5-7B-MeChat" \
        --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": "jun10k/Qwen1.5-7B-MeChat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jun10k/Qwen1.5-7B-MeChat with Docker Model Runner:
```
docker model run hf.co/jun10k/Qwen1.5-7B-MeChat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

本项目是用Qwen1.5-7B为基础，使用心理健康对话数据集实现微调。仅用于技术研究用途。

引用

本项目的使用来自中文心理健康支持对话 · 数据集(SmileChat) 其具体信息如下

@misc{qiu2023smile,
      title={SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support},
      author={Huachuan Qiu and Hongliang He and Shuai Zhang and Anqi Li and Zhenzhong Lan},
      year={2023},
      eprint={2305.00450},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Downloads last month: 9

Safetensors

Model size

8B params

Tensor type

F32

BF16

Model tree for jun10k/Qwen1.5-7B-MeChat

Quantizations

1 model

Paper for jun10k/Qwen1.5-7B-MeChat

SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support

Paper • 2305.00450 • Published Apr 30, 2023