Instructions to use TareksTesting/Dungeonmaster-Expanded-R1-LLaMa-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TareksTesting/Dungeonmaster-Expanded-R1-LLaMa-70B with Transformers:

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

pipe = pipeline("text-generation", model="TareksTesting/Dungeonmaster-Expanded-R1-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-Expanded-R1-LLaMa-70B")
model = AutoModelForCausalLM.from_pretrained("TareksTesting/Dungeonmaster-Expanded-R1-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-Expanded-R1-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-Expanded-R1-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-Expanded-R1-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TareksTesting/Dungeonmaster-Expanded-R1-LLaMa-70B

SGLang

How to use TareksTesting/Dungeonmaster-Expanded-R1-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-Expanded-R1-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-Expanded-R1-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-Expanded-R1-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-Expanded-R1-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TareksTesting/Dungeonmaster-Expanded-R1-LLaMa-70B with Docker Model Runner:
```
docker model run hf.co/TareksTesting/Dungeonmaster-Expanded-R1-LLaMa-70B
```

Tarek07 commited on Mar 5, 2025

Commit

5cd17ae

verified ·

1 Parent(s): 6336113

Update README.md

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@@ -13,8 +13,22 @@ tags:
 - merge
 license: llama3.3
 ---
 My ideal vision for Dungeonmaster were these 7 models. However I was concerned about that many models in one single merge. But you never know, so I decided to try both and see...
 NB: I think the reasoning got too diluted, it works well as a normal model, but 'thinking' doesn't seem to work.
 # Mergekit
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

 - merge
 license: llama3.3
 ---
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64909c086073a0cd172d0411/cySX1YUHKkB3R8oz7czE4.png)
+Dungeonmaster is meant to be specifically for creative roleplays with stakes and consequences using the following curated models:
 My ideal vision for Dungeonmaster were these 7 models. However I was concerned about that many models in one single merge. But you never know, so I decided to try both and see...
 NB: I think the reasoning got too diluted, it works well as a normal model, but 'thinking' doesn't seem to work.
+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 creativity
+TheDrummer/Anubis-70B-v1 - Another excellent RP fine-tune.
+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](https://github.com/cg123/mergekit).