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shawaz03
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Model Details
Model Provider
shawaz03
Model Tree
Qwen/Qwen2.5-Coder-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Run Vibe Coder on a Free Google Colab T4 GPU with zero memory warnings:
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Vibe Coder v2.0 MAX is a specialized, fine-tuned code generation model based on Qwen2.5-Coder-7B-Instruct. It is engineered specifically to eliminate common LLM coding pitfalls—such as lazy placeholder comments (// TODO: implement logic), broken imports, and outdated visual tropes.
bg-neutral-900, border-neutral-800), custom typography, responsive grid layouts, and Lucide React icons.Vibe Coder was trained on a 64,000-record Master Dataset structured in strict ChatML format across 7 specialized pipelines:
| Pipeline | Dataset Focus | Size |
|---|---|---|
| 1. Open-Source Repositories | Production Next.js server actions, Prisma schemas, Zustand stores | 25,000 records |
| 2. Handcrafted Vibe Templates | Complete Bento showcases, pricing matrices, checkout wizards, audio players | 12,000 records |
| 3. Multi-Turn Refinement | Multi-turn developer dialogues simulating feature additions and refactoring | 10,000 records |
| 4. Self-Healing & Debugging | Runtime errors, TypeScript compilation bugs, hydration fixes | 5,000 records |
| 5. Full-Stack Architectures | WebSocket chat rooms, Stripe webhook signature verifiers, Redis caching |
Qwen/Qwen2.5-Coder-7B-InstructrsLoRA = True (161.4M trainable parameters)0.035 – 0.045python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_id = "shawaz03/vibe-coder-7b-max" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.float16,) tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_id, quantization_config=bnb_config, device_map="auto", trust_remote_code=True) system_prompt = """You are Vibe Coder, a world-class principal full-stack software engineer and UI/UX designer.Write complete, modern, production-grade code in TypeScript, React, Next.js, and Node.js with ZERO placeholders.""" messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": "Build an interactive pricing matrix in React with Tailwind CSS, supporting monthly/annual toggle and feature checkmarks."}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( **inputs, max_new_tokens=2048, temperature=0.2, top_p=0.95, repetition_penalty=1.05, do_sample=True,) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))bash
vllm serve shawaz03/vibe-coder-7b-max --port 8000 --dtype float16This project is open-source and licensed under the Apache 2.0 License.
| 12,000 records |
98.5%import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_id = "shawaz03/vibe-coder-7b-max" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.float16,) tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_id, quantization_config=bnb_config, device_map="auto", trust_remote_code=True) system_prompt = """You are Vibe Coder, a world-class principal full-stack software engineer and UI/UX designer.Write complete, modern, production-grade code in TypeScript, React, Next.js, and Node.js with ZERO placeholders.""" messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": "Build an interactive pricing matrix in React with Tailwind CSS, supporting monthly/annual toggle and feature checkmarks."}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( **inputs, max_new_tokens=2048, temperature=0.2, top_p=0.95, repetition_penalty=1.05, do_sample=True,) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))vllm serve shawaz03/vibe-coder-7b-max --port 8000 --dtype float16