Instructions to use TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B with Transformers:

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

pipe = pipeline("text-generation", model="TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B")
model = AutoModelForCausalLM.from_pretrained("TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B

SGLang

How to use TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B 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 "TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B" \
    --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": "TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B",
		"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 "TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B" \
        --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": "TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B with Docker Model Runner:
```
docker model run hf.co/TareksTesting/Dungeonmaster-V2-Expanded-LLaMa-70B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Expanded V2 of Dungeonmaster, I decided to move away from the R1 base here, because I feel it the pros dont necessarily outweigh the cons. For V2 I decided to go for the classic nbeerbower/Llama-3.1-Nemotron-lorablated-70B as the base. Dungeonmaster expanded features 2 extra models, bringing the total up to 7! Admittedly I was concerned about that many models in one single merge. But you never know, so I decided to try both and see...

My ideal vision for Dungeonmaster were these 7 models.

LatitudeGames/Wayfarer-Large-70B-Llama-3.3 - A fine-tuned model specifically designed for this very application.
ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.3 - Another fine-tune trained on RP datasets.
Sao10K/70B-L3.3-mhnnn-x1 - For some extra unhinged creativity
TheDrummer/Anubis-70B-v1 - Another excellent RP fine-tune to help balance things out.
EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1 - For it's strong descriptive writing.
SicariusSicariiStuff/Negative_LLAMA_70B - To assist with the darker undertones.
TheDrummer/Fallen-Llama-3.3-R1-70B-v1 - The secret sauce, a completely unhinged thinking model that turns things up to 11.

Mergekit

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Linear DELLA merge method using nbeerbower/Llama-3.1-Nemotron-lorablated-70B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
    parameters:
      density: 0.7
  - model: ArliAI/Llama-3.3-70B-ArliAI-RPMax-v1.4
    parameters:
      density: 0.7
  - model: Sao10K/70B-L3.3-mhnnn-x1
    parameters:
      density: 0.7
  - model: LatitudeGames/Wayfarer-Large-70B-Llama-3.3
    parameters:
      density: 0.7
  - model: TheDrummer/Fallen-Llama-3.3-R1-70B-v1
    parameters:
      density: 0.7
  - model: TheDrummer/Anubis-70B-v1
    parameters:
      density: 0.7
  - model: SicariusSicariiStuff/Negative_LLAMA_70B
    parameters:
      density: 0.7
merge_method: della_linear
base_model: nbeerbower/Llama-3.1-Nemotron-lorablated-70B
parameters:
  weight: 0.14
  epsilon: 0.2
  lambda: 1.1
  normalize: true
dtype: bfloat16
tokenizer:
 source: nbeerbower/Llama-3.1-Nemotron-lorablated-70B