Instructions to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with Transformers:

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

pipe = pipeline("text-generation", model="samunder12/llama-3.1-8b-Rp-tadashinu-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("samunder12/llama-3.1-8b-Rp-tadashinu-gguf", dtype="auto")

PEFT
How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="samunder12/llama-3.1-8b-Rp-tadashinu-gguf",
	filename="Llama3Tadashinu.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf samunder12/llama-3.1-8b-Rp-tadashinu-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 samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf samunder12/llama-3.1-8b-Rp-tadashinu-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 samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M

Use Docker

docker model run hf.co/samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "samunder12/llama-3.1-8b-Rp-tadashinu-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": "samunder12/llama-3.1-8b-Rp-tadashinu-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M

SGLang

How to use samunder12/llama-3.1-8b-Rp-tadashinu-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 "samunder12/llama-3.1-8b-Rp-tadashinu-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": "samunder12/llama-3.1-8b-Rp-tadashinu-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 "samunder12/llama-3.1-8b-Rp-tadashinu-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": "samunder12/llama-3.1-8b-Rp-tadashinu-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with Ollama:
```
ollama run hf.co/samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M
```

Unsloth Studio new

How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf 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 samunder12/llama-3.1-8b-Rp-tadashinu-gguf 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 samunder12/llama-3.1-8b-Rp-tadashinu-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for samunder12/llama-3.1-8b-Rp-tadashinu-gguf to start chatting

Pi new

How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with Docker Model Runner:
```
docker model run hf.co/samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M
```

Lemonade

How to use samunder12/llama-3.1-8b-Rp-tadashinu-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull samunder12/llama-3.1-8b-Rp-tadashinu-gguf:Q4_K_M

Run and chat with the model

lemonade run user.llama-3.1-8b-Rp-tadashinu-gguf-Q4_K_M

List all available models

lemonade list

llama-3.1-8b-Rp-tadashinu-gguf - A dark , immersive , dialogue ready , High-Concept Storyteller and Roleplayer

Model Details

Base Model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Original LoRA Model: samunder12/llama-3.1-8b-roleplay-v5-lora
Fine-tuning Method: PEFT (LoRA) with Unsloth's performance optimizations.
LoRA Rank (r): 64
Format: GGUF
Quantization: Q4_K_M
context_window 4096

llama-3.1-8b-Rp-tadashinu-gguf is a fine-tuned version of Llama 3.1 8B Instruct, specifically crafted to be a master of high-concept, witty immersive , and darkly , intense creative writing.

This isn't your average storyteller. Trained on a curated dataset of absurd and imaginative scenarios—from sentient taxidermy raccoons to cryptid dating apps—this model excels at generating unique characters, crafting engaging scenes, and building fantastical worlds with a distinct, cynical voice. If you need a creative partner to brainstorm the bizarre, this is the model for you.

This model was fine-tuned using the Unsloth library for peak performance and memory efficiency.

Provided files:

LoRA adapter for use with the base model.
GGUF (q4_k_m) version for easy inference on local machines with llama.cpp, LM Studio, Ollama, etc.

💡 Intended Use & Use Cases

This model is designed for creative and entertainment purposes. It's an excellent tool for:

Story Starters: Breaking through writer's block with hilarious and unexpected premises.
Character Creation: Generating unique character bios with strong, memorable voices.
Scene Generation: Writing short, punchy scenes in a dark comedy or absurd fantasy style.
Roleplaying: Powering a game master or character with a witty, unpredictable personality.
Creative Brainstorming: Generating high-concept ideas for stories, games, or scripts.

🔧 How to Use

With Transformers (and Unsloth)

This model is a LoRA adapter. You must load it on top of the base model, unsloth/meta-llama-3.1-8b-instruct-bnb-4bit.

from unsloth import FastLanguageModel
from transformers import TextStreamer

model_repo = "samunder12/llama-3.1-8b-roleplay-v5-lora"
base_model_repo = "unsloth/meta-llama-3.1-8b-instruct-bnb-4bit"

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = model_repo,
    base_model = base_model_repo,
    max_seq_length = 4096,
    dtype = None,
    load_in_4bit = True,
)

# --- Your system prompt ----
system_prompt = "You are a creative and witty storyteller." # A simple prompt is best
user_message = "A timid barista discovers their latte art predicts the future. Describe a chaotic morning when their foam sketches start depicting ridiculous alien invasions."

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_message},
]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
text_streamer = TextStreamer(tokenizer)
_ = model.generate(inputs, streamer=text_streamer, max_new_tokens=512)

With GGUF The provided GGUF file (q4_k_m quantization) can be used with any llama.cpp compatible client, such as: LM Studio: Search for your model name samunder12/llama-3.1-8b-Rp-tadashinu-gguf directly in the app. Ollama: Create a Modelfile pointing to the local GGUF file. text-generation-webui: Place the GGUF file in your models directory and load it. Remember to use the correct Llama 3.1 Instruct prompt template.

📝 Prompting Format This model follows the official Llama 3.1 Instruct chat template. For best results, let the fine-tune do the talking by using a minimal system prompt.

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{your_system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{your_user_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>