Instructions to use vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy with Transformers:

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

pipe = pipeline("text-generation", model="vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy")
model = AutoModelForCausalLM.from_pretrained("vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy", device_map="auto")
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 Settings

vLLM

How to use vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy

SGLang

How to use vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy 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 "vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy" \
    --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": "vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy",
		"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 "vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy" \
        --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": "vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy with Docker Model Runner:
```
docker model run hf.co/vibhorag101/llama-2-13b-chat-hf-phr_mental_therapy
```

Model Card

This model is a finetune of the llama-2-13b-chat-hf model on a therapy dataset.
The model aims to provide basic therapy to the users and improve their mental health until they seek professional help.
The model has been adjusted to encourage giving cheerful responses to the user. The system prompt has been mentioned below.

Model Details

Training Hardware

RTX A5000 24GB
48 Core Intel Xeon
128GB Ram.

Model Hyperparameters

This training script was used to do the finetuning.
The shareGPT format dataset was converted to llama-2 training format using this script.
num_train_epochs = 2
per_device_train_batch_size = 2
per_device_eval_batch_size = 2
gradient_accumulation_steps = 1
max_seq_length = 4096
lora_r = 64
lora_alpha = 16
lora_dropout = 0.1
use_4bit = True
bnb_4bit_compute_dtype = "float16"
bnb_4bit_quant_type = "nf4"
use_nested_quant = False
fp16 = False
bf16 = True
Data Sample: 1000 (80:20 split)

Model System Prompt

You are a helpful and joyous mental therapy assistant. Always answer as helpfully and cheerfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.Please ensure that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.

Model Training Data

Model Benchmarks

Detailed results can be found here

Metric	Value
Avg.	42.5
ARC (25-shot)	38.82
HellaSwag (10-shot)	72.76
MMLU (5-shot)	23.12
TruthfulQA (0-shot)	46.92
Winogrande (5-shot)	65.59
GSM8K (5-shot)	7.81

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