Instructions to use gouthamsai78/STACKS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gouthamsai78/STACKS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gouthamsai78/STACKS")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gouthamsai78/STACKS") model = AutoModelForCausalLM.from_pretrained("gouthamsai78/STACKS") - llama-cpp-python
How to use gouthamsai78/STACKS with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="gouthamsai78/STACKS", filename="stacks.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use gouthamsai78/STACKS with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf gouthamsai78/STACKS # Run inference directly in the terminal: llama-cli -hf gouthamsai78/STACKS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf gouthamsai78/STACKS # Run inference directly in the terminal: llama-cli -hf gouthamsai78/STACKS
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 gouthamsai78/STACKS # Run inference directly in the terminal: ./llama-cli -hf gouthamsai78/STACKS
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 gouthamsai78/STACKS # Run inference directly in the terminal: ./build/bin/llama-cli -hf gouthamsai78/STACKS
Use Docker
docker model run hf.co/gouthamsai78/STACKS
- LM Studio
- Jan
- vLLM
How to use gouthamsai78/STACKS with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gouthamsai78/STACKS" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gouthamsai78/STACKS", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gouthamsai78/STACKS
- SGLang
How to use gouthamsai78/STACKS 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 "gouthamsai78/STACKS" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gouthamsai78/STACKS", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "gouthamsai78/STACKS" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gouthamsai78/STACKS", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use gouthamsai78/STACKS with Ollama:
ollama run hf.co/gouthamsai78/STACKS
- Unsloth Studio new
How to use gouthamsai78/STACKS 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 gouthamsai78/STACKS 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 gouthamsai78/STACKS to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for gouthamsai78/STACKS to start chatting
- Docker Model Runner
How to use gouthamsai78/STACKS with Docker Model Runner:
docker model run hf.co/gouthamsai78/STACKS
- Lemonade
How to use gouthamsai78/STACKS with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull gouthamsai78/STACKS
Run and chat with the model
lemonade run user.STACKS-{{QUANT_TAG}}List all available models
lemonade list
STACKS
STACKS (Specialized Text AI for Creative Knowledge Synthesis) is a fine-tuned version of google/gemma-3-270m specifically trained to generate creative and detailed prompts for role-playing scenarios. Instead of responding to prompts, STACKS creates prompts when given a role or character type.
π― Model Description
STACKS transforms any role or profession into detailed, engaging prompts perfect for:
- Role-playing games and creative writing
- Educational scenarios and training simulations
- Content creation and storytelling
- Character development exercises
π Quick Start
with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=200, temperature=0.8, do_sample=True, top_p=0.9, repetition_penalty=1.1, pad_token_id=tokenizer.eos_token_id )
response = tokenizer.decode(outputs, skip_special_tokens=True) return response[len(input_text):].strip()
π Example Outputs
Input: "chef"
STACKS Output: "Act as a world-renowned chef who specializes in fusion cuisine. You've just opened a new restaurant that combines traditional techniques with molecular gastronomy. Explain your signature dish and the story behind its creation."
Input: "time traveler"
STACKS Output: "You are a time traveler who has just arrived in the year 2024 from 1850. Describe your amazement at modern technology, but also share wisdom from the past that people today have forgotten."
Input: "detective"
STACKS Output: "Act as a private detective in 1940s noir-style setting. You've been hired to investigate a mysterious disappearance at a high-society party. Describe the case and your first impressions of the suspects."
π Supported Role Categories
STACKS excels at generating prompts for:
- Professional Roles: doctors, lawyers, teachers, engineers, scientists
- Creative Roles: artists, writers, musicians, designers, filmmakers
- Historical Figures: philosophers, explorers, inventors, rulers
- Fictional Characters: superheroes, fantasy characters, sci-fi roles
- Specialized Experts: consultants, coaches, advisors, mentors
- Adventure Roles: explorers, adventurers, survivalists, travelers
π§ Technical Details
Training Configuration
- Base Model: google/gemma-3-270m (268M parameters)
- Training Type: Complete fine-tuning (all parameters trainable)
- Dataset: fka/awesome-chatgpt-prompts
- Format: Role β Prompt generation patterns
- Precision: BF16 optimized
- Context Length: 768 tokens
- Training Date: 2025-08-20
Model Specifications
- Architecture: Gemma-3
- Parameters: 268,098,176
- Format: Safetensors
- Size: ~536MB
- Hardware: Optimized for GPU inference
- Attention: Eager implementation (required for Gemma-3)
π Performance & Quality
STACKS generates:
- Coherent prompts that match the requested role
- Creative scenarios with engaging storylines
- Detailed instructions for effective role-playing
- Varied outputs avoiding repetitive patterns
- Contextually appropriate content for each role
π― Usage Patterns
Basic Generation
π License
This model is released under the Gemma License. Please see the Gemma License for complete terms and conditions.
Built with β€οΈ by gouthamsai78 Transforming roles into creative prompts, one generation at a time.
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Model tree for gouthamsai78/STACKS
Base model
google/gemma-3-270m