Instructions to use maldv/dragonwar-7b-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maldv/dragonwar-7b-alpha with Transformers:

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

pipe = pipeline("text-generation", model="maldv/dragonwar-7b-alpha")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maldv/dragonwar-7b-alpha")
model = AutoModelForCausalLM.from_pretrained("maldv/dragonwar-7b-alpha")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maldv/dragonwar-7b-alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maldv/dragonwar-7b-alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maldv/dragonwar-7b-alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maldv/dragonwar-7b-alpha

SGLang

How to use maldv/dragonwar-7b-alpha 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 "maldv/dragonwar-7b-alpha" \
    --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": "maldv/dragonwar-7b-alpha",
		"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 "maldv/dragonwar-7b-alpha" \
        --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": "maldv/dragonwar-7b-alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use maldv/dragonwar-7b-alpha 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 maldv/dragonwar-7b-alpha 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 maldv/dragonwar-7b-alpha to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for maldv/dragonwar-7b-alpha to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="maldv/dragonwar-7b-alpha",
    max_seq_length=2048,
)

Docker Model Runner
How to use maldv/dragonwar-7b-alpha with Docker Model Runner:
```
docker model run hf.co/maldv/dragonwar-7b-alpha
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gguf quants

Dragonwar 7b - α

The time of the great dragon war is upon us! How many different fantasy novels? One hundred and seventeen you say?

Trained with full text windows, followed by completion, followed by ORPO, followed by one more epoch of the full text, rotated 1/4 in the window. That last train settled everything down and it seems quite coherent.

How to Use

This is not a chat model, but intended for storymode or similar. No prompt, but start with a bit of story, or a name.

*** Prologue

The sun rose

Authors notes are highly effective. You can use an authors note of something like:

[King Robb Stark and Lord Rahl are at war.]

You have quite a cast of characters to draw from. Perhaps Perrin makes a stop by the Waystone Inn, or Zeddicus and Gandalf have a smoke together.

Settings

I usually use Min-P of 0.1, dynatemp between 0.5 and 2, and smoothing between 0.05 and 0.2.

Hacks

To get rid of unwanted EOS's, I did the following...

import torch
result_dict : dict[str, torch.Tensor] = model.state_dict()
result_dict['lm_head.weight'][2] = 0
model.state_dict = lambda : result_dict

So now there are no EOS's at all, ever.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for maldv/dragonwar-7b-alpha

Quantizations

2 models

maldv
/

dragonwar-7b-alpha

Dragonwar 7b - α

How to Use

Settings

Hacks

Model tree for maldv/dragonwar-7b-alpha

Spaces using maldv/dragonwar-7b-alpha 9