Instructions to use cswan801/BlackSwan-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cswan801/BlackSwan-v5 with Transformers:

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

pipe = pipeline("text-generation", model="cswan801/BlackSwan-v5")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("cswan801/BlackSwan-v5")
model = AutoModelForImageTextToText.from_pretrained("cswan801/BlackSwan-v5")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cswan801/BlackSwan-v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cswan801/BlackSwan-v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cswan801/BlackSwan-v5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cswan801/BlackSwan-v5

SGLang

How to use cswan801/BlackSwan-v5 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 "cswan801/BlackSwan-v5" \
    --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": "cswan801/BlackSwan-v5",
		"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 "cswan801/BlackSwan-v5" \
        --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": "cswan801/BlackSwan-v5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cswan801/BlackSwan-v5 with Docker Model Runner:
```
docker model run hf.co/cswan801/BlackSwan-v5
```

BlackSwan v5

BlackSwan is a fine-tuned AI accountability partner built for The Underground Circle — a productivity app for dev teams.

Model Details

Base model: Qwen/Qwen3.5-4B
Fine-tuning: LoRA (rank 16, alpha 16) on 40K+ training examples
Training: 2500 iterations, final loss 6.3
Format: safetensors (bfloat16)
License: Apache 2.0

Personality

BlackSwan has quiet confidence — knowledgeable but never arrogant. Direct. No fluff, no corporate speak. Gives real feedback. Knows productivity, design, UI/UX, coding, architecture, and general knowledge.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("cswan801/BlackSwan-v5")
tokenizer = AutoTokenizer.from_pretrained("cswan801/BlackSwan-v5")

Training Data

Code instructions (CodeAlpaca, Evol-Instruct-Code)
General knowledge (OpenHermes 2.5, SlimOrca)
Multi-turn conversation (Capybara, UltraChat)
Math reasoning (GSM8K, MathInstruct)
App-specific data (terminal commands, agent activity, tasks, check-ins)

Downloads last month: 7

Safetensors

Model size

5B params

Tensor type

F32

BF16

Model tree for cswan801/BlackSwan-v5

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Adapter

(223)

this model