Instructions to use dkp2701/survuday_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dkp2701/survuday_v3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dkp2701/survuday_v3",
	filename="survuday_v3.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "{\n    \"question\": \"What is my name?\",\n    \"context\": \"My name is Clara and I live in Berkeley.\"\n}"
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dkp2701/survuday_v3 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dkp2701/survuday_v3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dkp2701/survuday_v3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dkp2701/survuday_v3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dkp2701/survuday_v3: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 dkp2701/survuday_v3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dkp2701/survuday_v3: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 dkp2701/survuday_v3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dkp2701/survuday_v3:Q4_K_M

Use Docker

docker model run hf.co/dkp2701/survuday_v3:Q4_K_M

LM Studio
Jan
Ollama
How to use dkp2701/survuday_v3 with Ollama:
```
ollama run hf.co/dkp2701/survuday_v3:Q4_K_M
```

Unsloth Studio

How to use dkp2701/survuday_v3 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dkp2701/survuday_v3 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dkp2701/survuday_v3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dkp2701/survuday_v3 to start chatting

Atomic Chat new
Docker Model Runner
How to use dkp2701/survuday_v3 with Docker Model Runner:
```
docker model run hf.co/dkp2701/survuday_v3:Q4_K_M
```

Lemonade

How to use dkp2701/survuday_v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dkp2701/survuday_v3:Q4_K_M

Run and chat with the model

lemonade run user.survuday_v3-Q4_K_M

List all available models

lemonade list

survuday

Mental Health Support Chatbot developed by fine-tuning Large Language Model - Llama 3.2
Version 3

Other Model Versions Huggingface Repo

survuday_v1: https://huggingface.co/dkp2701/survuday_v1
survuday_v2: https://huggingface.co/dkp2701/survuday_v2

Dataset

Mental Helath-related dialogue data were taken from kaggle and apart from that non-mental health-related conversations for model effectiveness were added manually.
Non-mental health-related data were added so that the model can get confined to give response only related to mental health conversations.
Synthetic Therapy Conversations (Kaggle): https://www.kaggle.com/datasets/thedevastator/synthetic-therapy-conversations-dataset

Downloads last month: 10

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for dkp2701/survuday_v3

Base model

meta-llama/Llama-3.2-3B

Quantized

(140)

this model