Instructions to use ArkMaster123/qwen2.5-7b-therapist-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArkMaster123/qwen2.5-7b-therapist-v2 with Transformers:

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

pipe = pipeline("text-generation", model="ArkMaster123/qwen2.5-7b-therapist-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ArkMaster123/qwen2.5-7b-therapist-v2")
model = AutoModelForMultimodalLM.from_pretrained("ArkMaster123/qwen2.5-7b-therapist-v2")
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 Settings

vLLM

How to use ArkMaster123/qwen2.5-7b-therapist-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArkMaster123/qwen2.5-7b-therapist-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArkMaster123/qwen2.5-7b-therapist-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ArkMaster123/qwen2.5-7b-therapist-v2

SGLang

How to use ArkMaster123/qwen2.5-7b-therapist-v2 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 "ArkMaster123/qwen2.5-7b-therapist-v2" \
    --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": "ArkMaster123/qwen2.5-7b-therapist-v2",
		"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 "ArkMaster123/qwen2.5-7b-therapist-v2" \
        --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": "ArkMaster123/qwen2.5-7b-therapist-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ArkMaster123/qwen2.5-7b-therapist-v2 with Docker Model Runner:
```
docker model run hf.co/ArkMaster123/qwen2.5-7b-therapist-v2
```

Qwen2.5-7B-Instruct Therapist v2

Fine-tuned version of Qwen/Qwen2.5-7B-Instruct for therapeutic conversations.

Model Details

Base Model: Qwen/Qwen2.5-7B-Instruct
Fine-tuning Method: LoRA (Rank 64) + merged into full model
Training Dataset: Jyz1331/therapist_conversations (250 conversations)
Training Infrastructure: Modal A100 GPU
Training Time: ~10 min on A100

Training Pipeline

Phase 1: SFT (LoRA r=64)

Learning rate: 2e-4, cosine schedule, 10% warmup
Effective batch size: 32 (4×8 gradient accumulation)
Epochs: 3
bf16 training, gradient checkpointing
Trainable params: ~340M (4.5% of total)

Phase 2: Merged

LoRA adapter merged into base model for single-file deployment

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ArkMaster123/qwen2.5-7b-therapist-v2")
tokenizer = AutoTokenizer.from_pretrained("ArkMaster123/qwen2.5-7b-therapist-v2")

messages = [{"role": "user", "content": "I'm feeling anxious about work"}]
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)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Safety Limitations

This model is not a replacement for professional mental health services. For crises, please contact emergency services or a crisis helpline.

Citation

@misc{therapist-v2,
  author = {ArkMaster123},
  title = {Qwen2.5-7B-Instruct Therapist v2},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ArkMaster123/qwen2.5-7b-therapist-v2}}
}

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Safetensors

Model size

8B params

Tensor type

BF16