Instructions to use kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification with Transformers:

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

pipe = pipeline("text-generation", model="kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification")
model = AutoModelForCausalLM.from_pretrained("kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification

SGLang

How to use kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification 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 "kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification" \
    --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": "kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification",
		"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 "kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification" \
        --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": "kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification with Docker Model Runner:
```
docker model run hf.co/kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-3.1-8B-Instruct-Mental-Health-Classification

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on an suchintikasarkar/sentiment-analysis-for-mental-health dataset.

Tutorial

Get started with the new Llama models and customize Llama-3.1-8B-It to predict various mental health disorders from the text by following the Fine-Tuning Llama 3.1 for Text Classification tutorial.

Use with Transformers

from transformers import AutoTokenizer,AutoModelForCausalLM,pipeline
import torch

model_id = "kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
        model_id,
        return_dict=True,
        low_cpu_mem_usage=True,
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True,
)

text = "I'm trapped in a storm of emotions that I can't control, and it feels like no one understands the chaos inside me"
prompt = f"""Classify the text into Normal, Depression, Anxiety, Bipolar, and return the answer as the corresponding mental health disorder label.
text: {text}
label: """.strip()

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipe(prompt, max_new_tokens=2, do_sample=True, temperature=0.1)

print(outputs[0]["generated_text"].split("label: ")[-1].strip())

# Depression

Results

100%|██████████| 300/300 [03:24<00:00,  1.47it/s]

Accuracy: 0.913
Accuracy for label Normal: 0.972
Accuracy for label Depression: 0.913
Accuracy for label Anxiety: 0.667
Accuracy for label Bipolar: 0.800

Classification Report:

              precision    recall  f1-score   support

      Normal       0.92      0.97      0.95       143
  Depression       0.93      0.91      0.92       115
     Anxiety       0.75      0.67      0.71        27
     Bipolar       1.00      0.80      0.89        15

    accuracy                           0.91       300
   macro avg       0.90      0.84      0.87       300
weighted avg       0.91      0.91      0.91       300

Confusion Matrix:

[[139   3   1   0]
 [  5 105   5   0]
 [  6   3  18   0]
 [  1   2   0  12]]

Downloads last month: 16

Safetensors

Model size

8B params

Tensor type

F16

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