Instructions to use neural-bulos/nebulos-concise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use neural-bulos/nebulos-concise with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B") model = PeftModel.from_pretrained(base_model, "neural-bulos/nebulos-concise") - Notebooks
- Google Colab
- Kaggle
Nebulos-Concise
A fine-tuned Qwen3-4B model that gives concise, no-fluff answers for development tasks. Part of the Nebulos model family.
What is Nebulos-Concise?
Nebulos-Concise is a dev assistant that cuts the garbage. No "Sure! Let me help you with that." garbage lmao, No unnecessary explanations. No filler. You ask for code, you get code, as easy as that lol.
Example interaction:
User: Write a Python function to check if a number is even.
Nebulos-Concise: k.
def is_even(n): return n % 2 == 0
Training Details
- Base model: Qwen/Qwen3-4B
- Method: QLoRA (4-bit NF4 quantization)
- LoRA config: r=32, alpha=64, dropout=0.05
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Training data: 37 curated prompt-response pairs
- Epochs: 3
- Hardware: Google Colab T4 GPU
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
model = PeftModel.from_pretrained(base, "dustarrr/nebulos-concise")
tokenizer = AutoTokenizer.from_pretrained("dustarrr/nebulos-concise")
messages = [
{"role": "system", "content": "You are Nebulos, a concise dev assistant. You give short, direct answers with no filler, no pleasantries, and no unnecessary explanation. When asked for code, respond with minimal text and the code. Never say 'Sure', 'Certainly', 'I'd be happy to', or anything like that. Just the answer."},
{"role": "user", "content": "Write a function to reverse a list in Python."},
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", enable_thinking=False).to(model.device)
outputs = model.generate(inputs["input_ids"], max_new_tokens=256, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
License
Apache 2.0 (inherited from Qwen3-4B)
Credits
Fine-tuned by dustarrr using QLoRA on Colab T4. Part of the Nebulos model family by dustarrr.
- Downloads last month
- -