Instructions to use JBHarris/dm-llm-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JBHarris/dm-llm-tiny with Transformers:

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

pipe = pipeline("text-generation", model="JBHarris/dm-llm-tiny")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JBHarris/dm-llm-tiny")
model = AutoModelForCausalLM.from_pretrained("JBHarris/dm-llm-tiny")
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]:]))

llama-cpp-python

How to use JBHarris/dm-llm-tiny with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="JBHarris/dm-llm-tiny",
	filename="dm-llm-tiny-Q4_K_M.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use JBHarris/dm-llm-tiny with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/JBHarris/dm-llm-tiny:Q4_K_M

LM Studio
Jan

vLLM

How to use JBHarris/dm-llm-tiny with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/JBHarris/dm-llm-tiny:Q4_K_M

SGLang

How to use JBHarris/dm-llm-tiny 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 "JBHarris/dm-llm-tiny" \
    --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": "JBHarris/dm-llm-tiny",
		"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 "JBHarris/dm-llm-tiny" \
        --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": "JBHarris/dm-llm-tiny",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use JBHarris/dm-llm-tiny with Ollama:
```
ollama run hf.co/JBHarris/dm-llm-tiny:Q4_K_M
```

Unsloth Studio new

How to use JBHarris/dm-llm-tiny 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 JBHarris/dm-llm-tiny 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 JBHarris/dm-llm-tiny to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for JBHarris/dm-llm-tiny to start chatting

Docker Model Runner
How to use JBHarris/dm-llm-tiny with Docker Model Runner:
```
docker model run hf.co/JBHarris/dm-llm-tiny:Q4_K_M
```

Lemonade

How to use JBHarris/dm-llm-tiny with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull JBHarris/dm-llm-tiny:Q4_K_M

Run and chat with the model

lemonade run user.dm-llm-tiny-Q4_K_M

List all available models

lemonade list

DM-LLM-Tiny

A tiny (1.1B parameter) language model fine-tuned for Dungeons & Dragons content generation.

What it does

Generates creative D&D content including:

NPCs — memorable characters with backstories, motivations, and quirks
Quests — hooks, outlines, and full quest arcs
Dialog — in-character conversations, monologues, and banter
Locations — vivid descriptions of dungeons, towns, and wilderness
Encounters — combat, social, and puzzle encounters

Usage

With Ollama (easiest)

ollama run JBHarris/dm-llm-tiny

With Transformers

from transformers import pipeline

pipe = pipeline("text-generation", model="JBHarris/dm-llm-tiny")
messages = [
    {"role": "system", "content": "You are a creative D&D dungeon master's assistant."},
    {"role": "user", "content": "Create a mysterious NPC for a tavern scene."},
]
result = pipe(messages, max_new_tokens=512)
print(result[0]["generated_text"][-1]["content"])

Training

Base model: TinyLlama-1.1B-Chat-v1.0
Method: QLoRA (4-bit NF4 quantization + LoRA r=64)
Data: ~5000 synthetic D&D instruction/response pairs generated with Claude
Hardware: NVIDIA RTX 4080 16GB

Limitations

This is a 1.1B parameter model. It's creative and fun for brainstorming but will not match the quality of larger models (7B+). Best used as a quick idea generator, not a replacement for a human DM's judgment.

License

Apache 2.0 (same as base model)

Downloads last month: 173

Safetensors

Model size

1B params

Tensor type

F16

Model tree for JBHarris/dm-llm-tiny

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Quantized

(144)

this model