Instructions to use nayaksomkar/Qwen3-0.6B-PsychLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nayaksomkar/Qwen3-0.6B-PsychLM with Transformers:

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

pipe = pipeline("text-generation", model="nayaksomkar/Qwen3-0.6B-PsychLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nayaksomkar/Qwen3-0.6B-PsychLM")
model = AutoModelForCausalLM.from_pretrained("nayaksomkar/Qwen3-0.6B-PsychLM")
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 nayaksomkar/Qwen3-0.6B-PsychLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nayaksomkar/Qwen3-0.6B-PsychLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nayaksomkar/Qwen3-0.6B-PsychLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nayaksomkar/Qwen3-0.6B-PsychLM

SGLang

How to use nayaksomkar/Qwen3-0.6B-PsychLM 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 "nayaksomkar/Qwen3-0.6B-PsychLM" \
    --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": "nayaksomkar/Qwen3-0.6B-PsychLM",
		"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 "nayaksomkar/Qwen3-0.6B-PsychLM" \
        --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": "nayaksomkar/Qwen3-0.6B-PsychLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nayaksomkar/Qwen3-0.6B-PsychLM with Docker Model Runner:
```
docker model run hf.co/nayaksomkar/Qwen3-0.6B-PsychLM
```

🧠 Psychology Quality Dataset

A curated, cleaned dataset for training AI models on psychology and mental health conversations.

📌 Overview

The Psychology Quality Dataset combines multiple Hugging Face datasets into a unified user/assistant conversational format, optimized for fine-tuning LLMs in mental health and psychology domains.

🔗 Dataset

https://huggingface.co/datasets/nayaksomkar/PsychologyQualityDataset

📊 Sources

Gragroo/psy_conv_v1_gpt1
Gragroo/psy_conv_v1_gpt2
Gragroo/psy_conv_v1_gpt3
psycode1/psyset
Gragroo/psychology-question-answer_psygpt_format

🔧 Processing

Standardized to user/assistant format
Removed duplicates
Filtered harmful content (e.g., self-harm, violence)
Kept therapeutic responses (e.g., support, anxiety, coping)

📁 Format

{
  "conversations": [
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ]
}

🎯 Use Cases

Mental health chatbots
Therapy-style AI assistants
Psychology NLP research

⚠️ Note

Not a substitute for professional medical advice. Use with safety mechanisms in production.

📜 License

Refer to original datasets for licensing.

Downloads last month: 236

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for nayaksomkar/Qwen3-0.6B-PsychLM

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

unsloth/Qwen3-0.6B

Finetuned

(238)

this model

nayaksomkar
/

Qwen3-0.6B-PsychLM

🧠 Psychology Quality Dataset

📌 Overview

🔗 Dataset

📊 Sources

🔧 Processing

📁 Format

🎯 Use Cases

⚠️ Note

📜 License

Model tree for nayaksomkar/Qwen3-0.6B-PsychLM

Datasets used to train nayaksomkar/Qwen3-0.6B-PsychLM