Instructions to use QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF", device_map="auto")

PEFT
How to use QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF 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 "QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF" \
    --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": "QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF",
		"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 "QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF" \
        --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": "QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF

This is quantized version of prabureddy/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2 created using llama.cpp

Original Model Card

Model Trained Using AutoTrain

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the mental_health_counseling_conversations dataset.

Usage


from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "prabureddy/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2"

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

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "Hey Alex! 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')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "Hello! How can I assist you today?"
print(response)

Downloads last month: 577

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF

Base model

mistralai/Mistral-7B-Instruct-v0.2

Quantized

(109)

this model

QuantFactory
/

Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF

QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF

Original Model Card

Model Trained Using AutoTrain

Usage

Model tree for QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF

Dataset used to train QuantFactory/Mental-Health-FineTuned-Mistral-7B-Instruct-v0.2-GGUF