Instructions to use GRMenon/mental-mistral-7b-instruct-autotrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use GRMenon/mental-mistral-7b-instruct-autotrain with PEFT:
```
Task type is invalid.
```

How to use GRMenon/mental-mistral-7b-instruct-autotrain with Transformers:

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

pipe = pipeline("text-generation", model="GRMenon/mental-mistral-7b-instruct-autotrain")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GRMenon/mental-mistral-7b-instruct-autotrain")
model = AutoModelForCausalLM.from_pretrained("GRMenon/mental-mistral-7b-instruct-autotrain")
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 GRMenon/mental-mistral-7b-instruct-autotrain with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GRMenon/mental-mistral-7b-instruct-autotrain

SGLang

How to use GRMenon/mental-mistral-7b-instruct-autotrain 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 "GRMenon/mental-mistral-7b-instruct-autotrain" \
    --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": "GRMenon/mental-mistral-7b-instruct-autotrain",
		"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 "GRMenon/mental-mistral-7b-instruct-autotrain" \
        --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": "GRMenon/mental-mistral-7b-instruct-autotrain",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GRMenon/mental-mistral-7b-instruct-autotrain with Docker Model Runner:
```
docker model run hf.co/GRMenon/mental-mistral-7b-instruct-autotrain
```

Model Trained Using AutoTrain

This model was trained using AutoTrain and is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the mental_health_counseling_conversations dataset.
For more information, please visit AutoTrain.

Model description

A Mistral-7B-Instruct-v0.2 model finetuned on a corpus of mental health conversations between a psychologist and a user.
The intention was to create a mental health assistant, "Connor", to address user questions based on responses from a psychologist.

Training data

The model is finetuned on a corpus of mental health conversations between a psychologist and a client, in the form of context - response pairs. This dataset is a collection of questions and answers sourced from two online counseling and therapy platforms. The questions cover a wide range of mental health topics, and the answers are provided by qualified psychologists.
Dataset found here :-

Training hyperparameters

The following hyperparameters were used during training: TODO

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "GRMenon/mental-mistral-7b-instruct-autotrain"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

device = "cuda" if torch.cuda.is_available() else "cpu"

# Prompt content:
messages = [
    {"role": "user", "content": "Hey Connor! I have been feeling a bit down lately. I could really use some advice on how to feel better?"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages,
                                          tokenize=True,
                                          add_generation_prompt=True,
                                          return_tensors='pt').to(device)
output_ids = model.generate(input_ids=input_ids,
                            max_new_tokens=512,
                            do_sample=True,
                            pad_token_id=2)
response = tokenizer.batch_decode(output_ids.detach().cpu().numpy(),
                                  skip_special_tokens = True)

# Model response: 
print(response[0])

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

F16

GRMenon
/

mental-mistral-7b-instruct-autotrain

Model Trained Using AutoTrain

Model description

Training data

Training hyperparameters

Usage

Dataset used to train GRMenon/mental-mistral-7b-instruct-autotrain

Space using GRMenon/mental-mistral-7b-instruct-autotrain 1