Instructions to use skdrx/dracula-flow-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skdrx/dracula-flow-base with Transformers:

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

pipe = pipeline("text-generation", model="skdrx/dracula-flow-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("skdrx/dracula-flow-base")
model = AutoModelForCausalLM.from_pretrained("skdrx/dracula-flow-base")
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 skdrx/dracula-flow-base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/skdrx/dracula-flow-base

SGLang

How to use skdrx/dracula-flow-base 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 "skdrx/dracula-flow-base" \
    --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": "skdrx/dracula-flow-base",
		"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 "skdrx/dracula-flow-base" \
        --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": "skdrx/dracula-flow-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use skdrx/dracula-flow-base with Docker Model Runner:
```
docker model run hf.co/skdrx/dracula-flow-base
```

skdrx commited on Jan 8

Commit

6963f5a

verified ·

1 Parent(s): 0e727a9

Update README.md

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README.md +8 -4

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 ---
 base_model: Qwen/Qwen3-1.7B-Base
 library_name: transformers
-model_name: qwen3-sft-output
 tags:
 - generated_from_trainer
 - sft
@@ -9,19 +9,23 @@ tags:
 licence: license
 ---
-# Model Card for qwen3-sft-output
 This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start
 ```python
 from transformers import pipeline
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
 generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
 print(output["generated_text"])
 ```

 ---
 base_model: Qwen/Qwen3-1.7B-Base
 library_name: transformers
+model_name: dracula-flow-base
 tags:
 - generated_from_trainer
 - sft
 licence: license
 ---
+# Model Card for dracula-flow-base
 This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base).
 It has been trained using [TRL](https://github.com/huggingface/trl).
+This has been specifically trained on Dracula flow 1-5 for 6 epochs to get it to be semi decent enough at writing dracula flow bars. It is not advised to use it as an actual dracula flow generator but rather as a generator for synthetic data to then train the real dracula flow model.
 ## Quick start
 ```python
 from transformers import pipeline
+prompt = "[dracula flow]: "
 generator = pipeline("text-generation", model="None", device="cuda")
+output = generator(prompt, max_new_tokens=128, return_full_text=False)[0]
 print(output["generated_text"])
 ```