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
Local Apps Settings

llama.cpp

How to use JBHarris/dm-llm-tiny 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 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 serve -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

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

Atomic Chat new
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

JBHarris commited on May 5

Commit

6fbc64b

verified ·

1 Parent(s): c065bef

Update README.md

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README.md +65 -65

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@@ -1,65 +1,65 @@
----
-language:
-- en
-license: apache-2.0
-library_name: transformers
-tags:
-- dnd
-- dungeons-and-dragons
-- rpg
-- text-generation
-- qlora
-- tinyllama
-base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
-pipeline_tag: text-generation
----
-# 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)
-```bash
-ollama run JBHarris/dm-llm-tiny
-```
-### With Transformers
-```python
-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:** ~500 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)

+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- dnd
+- dungeons-and-dragons
+- rpg
+- text-generation
+- qlora
+- tinyllama
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+pipeline_tag: text-generation
+---
+# 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)
+```bash
+ollama run JBHarris/dm-llm-tiny
+```
+### With Transformers
+```python
+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)