Instructions to use ShivomH/Elixir-MentalHealth-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ShivomH/Elixir-MentalHealth-LoRA with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "ShivomH/Elixir-MentalHealth-LoRA")

Transformers

How to use ShivomH/Elixir-MentalHealth-LoRA with Transformers:

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

pipe = pipeline("text-generation", model="ShivomH/Elixir-MentalHealth-LoRA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ShivomH/Elixir-MentalHealth-LoRA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ShivomH/Elixir-MentalHealth-LoRA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ShivomH/Elixir-MentalHealth-LoRA

SGLang

How to use ShivomH/Elixir-MentalHealth-LoRA 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 "ShivomH/Elixir-MentalHealth-LoRA" \
    --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": "ShivomH/Elixir-MentalHealth-LoRA",
		"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 "ShivomH/Elixir-MentalHealth-LoRA" \
        --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": "ShivomH/Elixir-MentalHealth-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ShivomH/Elixir-MentalHealth-LoRA with Docker Model Runner:
```
docker model run hf.co/ShivomH/Elixir-MentalHealth-LoRA
```

Model Details

This is just a LoRA Adapter, please navigate to ShivomH/Elixir-MentalHealth-3B to access the merged model with a guided inference script.

Model Description

Elixir-MentalHealth is a fine-tuned version of Meta-Llama-3.2-3B-Instruct, adapted using QLoRA on a curated dataset of single-turn and multi-turn mental health support conversations. The model is designed to provide empathetic, safe, and supportive responses while maintaining clear professional boundaries.

⚠️ Disclaimer: This model is not a replacement for professional mental health services. Always seek help from licensed professionals in crisis situations.

Primary Use Cases:

Mental health support chats
Stress and Anxiety management conversations
Empathetic listening, encouragement and general guidance
Psychoeducational tips (e.g., mindfulness, coping strategies, depression support)

Out-of-Scope Use (should NOT be used for):

Medical diagnosis or treatment planning
Emergency mental health intervention (e.g., suicide prevention crisis line replacement)
Legal, financial, or unrelated domains

This model is best suited for research, prototyping, and supportive chatbot applications where professional disclaimers and human oversight are always present.

How to Get Started with the Model

# Load model with LoRA
from peft import PeftModel, PeftConfig

lora_model = "ShivomH/Elixir-MentalHealth-3B"
base_model = "meta-llama/Llama-3.2-3B-Instruct"

# Load configuration
peft_config = PeftConfig.from_pretrained(lora_model)

# Load base model
inference_model = AutoModelForCausalLM.from_pretrained(
    peft_config.base_model,
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

# Load LoRA weights
inference_model = PeftModel.from_pretrained(inference_model, lora_model)

# Load tokenizer
inference_tokenizer = AutoTokenizer.from_pretrained(lora_model)

📊 Dataset Details

Dataset Source: ShivomH/MentalHealth-Support
Size: 25,000 conversations
Training Split: 23,750 (95%)
Validation Split: 1,250 (5%)
Multi-Turn Conversations: 16,000
Long Single-Turn Conversations: 8,000
Short Single-Turn Conversations: 1,000
Total tokens: ~17M
Mean: ~700 tokens
Data format: (.jsonl) Messages List with Roles and Content

General Details

Developed by: Shivom Hatalkar
Funded by: Shivom Hatalkar
Model type: NLP Text Generation LLM
Language(s) (NLP): English
License: llama3.2
Base Model: meta-llama/Llama-3.2-3B-Instruct

Model Sources

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Training Details

Please visit the Merged model ShivomH/Elixir-MentalHealth-3B page for detailed Training details.

Results

Please visit the Merged model ShivomH/Elixir-MentalHealth-3B page for viewing the testing samples.

Model Examination [optional]

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Framework versions

PEFT 0.17.1

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