Instructions to use Netuoso/qwepus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Netuoso/qwepus with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Netuoso/qwepus")
model = AutoModelForCausalLM.from_pretrained("Netuoso/qwepus", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Netuoso/qwepus with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Netuoso/qwepus

SGLang

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

Docker Model Runner
How to use Netuoso/qwepus with Docker Model Runner:
```
docker model run hf.co/Netuoso/qwepus
```

Qwepus

empero-ai/Qwythos-9B-v2 fine-tuned with LoRA (bf16, no quantization) on 96 long-form engineering examples: a hard practical task, analysis, complete code and verification (tests, sanitizers, measurements, mutation checks) in C++, C, Rust, Go, Python, TypeScript, SQL and HTML/JS.

System prompt used in training:

You are Qwepus, an elite software architect and systems engineer. You write robust, highly optimized, and mathematically sound code.

Training


LoRA	r=64, alpha=128, dropout=0.05, all linear layers of the decoder
Schedule	3 epochs, 8 examples per step, AdamW lr 0.0001, cosine, warmup 8%
Loss	answer tokens only, token-weighted
Sequence length	33995 tokens: the longest example, nothing truncated; fits the model's context (1048576)
Data	96 examples, 881,776 tokens per epoch
Loss before / after	0.9691 / 0.7293

The repository holds the merged bf16 model; lora/ holds the adapter alone.

Use

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Netuoso/qwepus")
model = AutoModelForCausalLM.from_pretrained("Netuoso/qwepus", dtype="bfloat16", device_map="auto")
messages = [{"role": "system", "content": "You are Qwepus, an elite software architect and systems engineer. You write robust, highly optimized, and mathematically sound code."},
            {"role": "user", "content": "..."}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(ids, max_new_tokens=4096)[0, ids.shape[1]:], skip_special_tokens=True))