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neural-bulos
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Model Details
Model Provider
neural-bulos
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
Nebulos-Concise is a dev assistant that cuts the garbage. No "Sure! Let me help you with that." No unnecessary explanations. No filler. You ask for code, you get code.
Example:
User: Write a Python function to check if a number is even.
Nebulos-Concise: k.
python
def is_even(n): return n % 2 == 0
python
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("neural-bulos/nebulos-concise")tokenizer = AutoTokenizer.from_pretrained("neural-bulos/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)input_ids = inputs["input_ids"].to(model.device)outputs = model.generate(input_ids, max_new_tokens=256, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id)print(tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True))A GGUF quantized version is available in this repository as model-q4_k_m.gguf.
Apache 2.0 (inherited from Qwen3-4B)
Fine-tuned by neural-bulos using QLoRA on Colab T4.
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("neural-bulos/nebulos-concise")tokenizer = AutoTokenizer.from_pretrained("neural-bulos/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)input_ids = inputs["input_ids"].to(model.device)outputs = model.generate(input_ids, max_new_tokens=256, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id)print(tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True))